Clustering and dynamic re-clustering of similar textual documents

ABSTRACT

A computer-implemented method includes obtaining a plurality of textual records divided into clusters and a residual set of the textual records, where a machine learning (ML) clustering model has divided the plurality of textual records into the clusters based on a similarity metric. The method also includes receiving, from a client device, a particular textual record representing a query and determining, by way of the ML clustering model and based on the similarity metric, that the particular textual record does not fit into any of the clusters. The method additionally includes, in response to determining that the particular textual record does not fit into any of the clusters, adding the particular textual record to the residual set of the textual records. The method can additionally include identifying, by way of the ML clustering model, that the residual set of the textual records contains a further cluster.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of and incorporates by reference thecontent of U.S. Non-Provisional Pat. App. No. 62/843,007, filed May 3,2019, in its entirety.

BACKGROUND

It is beneficial in a variety of applications to cluster samples of textinto groups that are similar. This can be done in order to facilitatetextual searching by identifying inter-related groups of text sampleswithin a corpus of text samples. Such grouping can facilitate textsearch or other text-related processing by limiting the search to textsamples within a cluster of related text samples (e.g., a cluster oftext samples that are similar to a query text string), avoiding the timeand computational cost of searching through the text records that arenot within the cluster of related text samples. Additionally,identifying such clusters within the textual portions of records thatinclude non-textual information (e.g., that include geographicalinformation, time stamps, weather-related information, informationrelated to the performance or operation of a technological system) mayfacilitate operations based on the non-textual information.

SUMMARY

Natural language processing or other methods can be used to generateclusters of text samples and to identify, for individual text samples,one or more clusters to which the individual text sample should beassigned. This can be done based on a determined similarity between thetext samples and/or between a text sample and the text samples in acluster. This clustering can be done in order to facilitate searchingwithin the text samples (e.g., by limiting the sample-level searching totext samples within an identified cluster relevant to a search query),to facilitate modeling of a managed network whose operations are relatedto the text samples, to identify and/or automatically implement a courseof action that has been effective when implemented in response to othertext samples assigned to a cluster, to allow all of the incident reportsassociated with text samples in a particular cluster to be resolved orto be otherwise manipulated as a group, or to provide some otherbenefit.

In one possible example, a query can include a sample of text describinga problem that a user is experiencing. A machine learning (ML) model orsome other algorithm could then be applied to assign the query to acluster of other textual records (e.g., other queries, incident reportsthat include queries and/or resolutions thereof, knowledgebase articles)that are similar to the query. Information related to the selectedcluster could then be provided to a user and/or to a technician. Suchinformation could include knowledgebase articles, resolved similarincident reports, or some other information that could provide asolution and/or answer to the query. Additionally or alternatively,similar queries or other information in the selected cluster could beprovided in order to facilitate determination of a solution and/oranswer to the query. This clustering may improve the quality of resultsprovided to the user and/or reduce the amount of time spent by the userbefore determining and implementing a solution to the problemrepresented by the query.

However, implementing such clustering can be computationally expensive,requiring extensive processor cycles, memory, or other resources togenerate an ML clustering model based on a training corpus of textualrecords. Accordingly, ML models may be re-generated, based on updatedtraining sets of textual records, according to a regular schedule. Usersmay manually specify clusters that have value and the specified clusterscan be retained through re-generation of the ML model in order to retainthe benefits of the specified clusters. For example, an identifiedcluster could correspond to an ongoing event and/or to a class ofincidents that the user is likely to continue to experience. Retaining aspecified cluster can include defining one of the clusters in there-generated ML model based on the specified cluster. For example, acluster in the re-generated ML model could be defined based on theidentity of training textual samples belonging to the specified cluster,based on a centroid or other information defining the specified cluster,or based on some other characteristic of the specified cluster.Additionally or alternatively, retaining a specified cluster can includeusing information about the specified cluster as a seed in there-generation of the ML model.

Between scheduled re-generations of the ML model, additional textualrecords (e.g., newly-generated incident reports) can be accumulated.These additional records may belong to additional clusters that are notreflected in the ML model, and so may be incorrectly assigned toclusters of the ML model and/or assigned to a set of residual textualrecords. To avoid the computational cost of completely re-generating theML model to include the additional textual records, the ML model may beupdated via an iterative update algorithm to take into account theinformation represented by the additional textual records.

This can include attempting to form additional clusters of textualrecords from within a set of residual textual records. When a newtextual record is received, it can be compared to the existing clusters.If the new textual record does not fit any of the existing clusters(e.g., by being less similar to each of the clusters than a specifiedthreshold similarity), the new textual records could be assigned to aset of residual textual records. Accordingly, the set of residualtextual records comes to include textual records that were determined,by the ML model, not to belong to any of the clusters defined by the MLmodel. If one or more additional clusters are identified within theresiduals, the ML model can be updated to include the additionalclusters. In addition, any textual records, from within the set ofresidual textual records, that correspond to the identified additionalclusters can be re-assigned from the set of residuals to the additionalclusters.

In some examples, user(s) may manually specify, a prioiri, conditionsunder which cluster formation should happen. Such manually-specifiedconditions may be specified according to a geographical location, a tagor other metadata, the identity of an individual associated with thetextual record (e.g., a user who generated an incident report that formsa part of the textual record), or according to some other consideration.Further clusters may then be identified within the user-specifiedconditions. This manual pre-clustering can provide a variety ofbenefits. For example, the clustering algorithm may perform moreefficiently by operating on fewer textual records. These methods canalso provide benefits by aligning the identified clusters with anorganization's considerations such that the generated clusters arealigned with those considerations. Additionally, the clusters generatedwithin a user-specified condition may be improved due to thepre-partitioning made possible by the user-specified conditions.

Transmitting a representation of one or more textual records in a corpusof textual records can include transmitting a variety of differentinformation. In some examples, transmitting a representation of atextual record includes transmitting an identification number (e.g., aglobal unique identifier (GUID)), time and date stamp, a location withina database, or some other identifying information that can enable anend-user instance to identify the represented textual record in adatabase or to otherwise access the represented textual record.Additionally or alternatively, transmitting a representation of atextual record can include transmitting a copy of the textual recorditself or a portion thereof. For example, transmitting a representationof a textual record can include transmitting a copy of a ‘problemresolution’ field of an incident report.

Transmitting a representation of one or more textual records in a corpusof textual records can include transmitting a representation of one ormore clusters of the textual records. This can include transmitting anidentification number (e.g., a GUID), a location within a database, orsome other identifying information that can enable the end-user instanceto identify the represented cluster of textual records. Additionally oralternatively, a representation of the contents of the cluster oftextual records could be transmitted. This could include transmittingidentification information for the textual records within the cluster(e.g., GUIDs, locations within a database), all or part of the contentsof the textual records within the cluster (e.g., text from ‘problemresolution’ fields of incident reports within the cluster), the contentsof one or more representative textual records associated with thecluster (e.g., a knowledgebase article associated with the cluster, a‘problem resolution’ field or other contents of an incident report thatis near a centroid of the cluster), or some other content associatedwith the cluster.

Note that the methods described herein to determine similarity betweentextual records, and to perform clustering based on such determinedsimilarities, can be applied to other types of records. For example,records including both textual aspects (e.g., text fields of an incidentreport that describe a problem, a solution to the problem, or some otherinformation) and non-textual aspects (e.g., dates, geographicallocations, enumerated categories, an identifier related to a user ortechnician, or some other non-textual information of an incident report)could be clustered by an ML model based on both the textual andnon-textual information. Indeed, the methods described herein could beapplied to determine numerical similarities and/or to perform clusteringbased on such determined numerical similarities for completelynon-textual records (e.g., combined genotype/phenotype information forindividuals receiving a treatment, historical usage statistics for usersof a media or network service, etc.).

Accordingly, a first example embodiment may involve acomputer-implemented method that includes: (i) receiving, by aprediction computational instance and from an end-user computationalinstance, a first textual record, wherein the end-user computationalinstance is dedicated to a managed network, and wherein the predictioncomputational instance and the end-user computational instance are bothdisposed within a remote network management platform; (ii) determining,by an ML clustering model of the prediction computational instance thatrepresents a set of clusters of textual records, that the first textualrecord corresponds to a particular cluster in the set of clusters oftextual records; (iii) transmitting, by the prediction computationalinstance and to the end-user computational instance, a representation ofthe particular cluster, (iv) receiving, by the prediction computationalinstance and from the end-user computational instance, a second textualrecord; (v) determining, by the ML clustering model, that the secondtextual record does not correspond to any cluster of textual records ofthe set of clusters of textual records; (vi) responsive to determiningthat the second textual record does not correspond to any cluster oftextual records of the set of clusters of textual records, adding, bythe prediction computational instance, the second textual record to astored set of residual textual records; (vii) identifying, by theprediction computational instance, an additional cluster of textualrecords based on the stored set of residual textual records; and (viii)transmitting, by the prediction computational instance and to theend-user computational instance, a representation of the additionalcluster of textual records.

In a second example embodiment, a computer-implemented method includes:(i) receiving a first plurality of textual records; (ii) determining,based on the plurality of textual records, a first ML clustering modelthat represents a first set of clusters of textual records, wherein eachcluster of the first ML clustering model corresponds to a respective setof textual records within the first plurality of textual records; (iii)receiving additional textual records; (iv) using the additional textualrecords to update the first ML clustering model via an iterative updateprocess; (v) receiving an indication of a preferred cluster of the firstset of clusters of textual records represented by the first MLclustering model; (vi) determining that a model refresh criterion issatisfied; and (vii) responsive to determining that the model refreshcriterion is satisfied, determining, based on the additional textualrecords, a second ML clustering model that represents a second set ofclusters of textual records, wherein each cluster of the second MLclustering model corresponds to a respective set of textual recordswithin the additional textual records, and wherein a first clusterwithin the second set of clusters of textual records of the second MLclustering model is determined based on the preferred cluster.

In a third example embodiment, a computer-implemented method includes:(i) obtaining a plurality of textual records divided into clusters and aresidual set of the textual records, wherein an ML clustering modeldivided the plurality of textual records based on a similarity metric;(ii) receiving, from a client device, a particular textual recordrepresenting a query; (iii) determining, by way of the ML clusteringmodel and based on the similarity metric, that the particular textualrecord does not fit into any of the clusters; and (iv) in response todetermining that the particular textual record does not fit into any ofthe clusters, adding the particular textual record to the residual setof the textual records

In a fourth example embodiment, an article of manufacture may include anon-transitory computer-readable medium, having stored thereon programinstructions that, upon execution by a computing system, cause thecomputing system to perform operations in accordance with the first,second, or third example embodiments.

In a fifth example embodiment, a computing system may include at leastone processor, as well as memory and program instructions. The programinstructions may be stored in the memory, and upon execution by the atleast one processor, cause the computing system to perform operations inaccordance with the first, second, or third example embodiments.

In a sixth example embodiment, a system may include various means forcarrying out each of the operations of the first, second, or thirdexample embodiments.

These, as well as other embodiments, aspects, advantages, andalternatives, will become apparent to those of ordinary skill in the artby reading the following detailed description, with reference whereappropriate to the accompanying drawings. Further, this summary andother descriptions and figures provided herein are intended toillustrate embodiments by way of example only and, as such, thatnumerous variations are possible. For instance, structural elements andprocess steps can be rearranged, combined, distributed, eliminated, orotherwise changed, while remaining within the scope of the embodimentsas claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a schematic drawing of a computing device, inaccordance with example embodiments.

FIG. 2 illustrates a schematic drawing of a server device cluster, inaccordance with example embodiments.

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments.

FIG. 4 depicts a communication environment involving a remote networkmanagement architecture, in accordance with example embodiments.

FIG. 5A depicts another communication environment involving a remotenetwork management architecture, in accordance with example embodiments.

FIG. 5B is a flow chart, in accordance with example embodiments.

FIG. 6 depicts an incident report, in accordance with exampleembodiments.

FIG. 7 depicts a database query architecture, in accordance with exampleembodiments.

FIG. 8A depicts an artificial neural network (ANN) configured forlearning the contextual meanings of words, in accordance with exampleembodiments.

FIG. 8B depicts a set of training data for the ANN of FIG. 9A, inaccordance with example embodiments.

FIG. 9A depicts an artificial neural network (ANN) configured forlearning the contextual meanings of words, in accordance with exampleembodiments.

FIG. 9B depicts a set of training data for the ANN of FIG. 9A, inaccordance with example embodiments.

FIG. 10A depicts training an ANN for paragraph vectors, in accordancewith example embodiments.

FIG. 10B depicts training an ANN for paragraph vectors, in accordancewith example embodiments.

FIG. 10C depicts training an ANN for paragraph vectors, in accordancewith example embodiments.

FIG. 10D depicts using a trained ANN to determine the paragraph vectorof a previously unseen paragraph, in accordance with exampleembodiments.

FIG. 11A depicts the locations of records in a two-dimensional space, inaccordance with example embodiments.

FIG. 11B depicts the records of FIG. 11A, having been grouped intoclusters, in accordance with example embodiments.

FIG. 11C depicts the records of FIG. 11A, having been grouped intoclusters, in accordance with example embodiments.

FIG. 11D depicts the records of FIG. 11A, having been grouped intoclusters, in accordance with example embodiments.

FIG. 11E depicts the records of FIG. 11A, having been grouped intoclusters, in accordance with example embodiments.

FIG. 11F depicts the records of FIG. 11A, having been grouped intoclusters, in accordance with example embodiments.

FIG. 12A depicts the locations of records in a two-dimensional space, inaccordance with example embodiments.

FIG. 12B depicts the records of FIG. 12A, having been grouped intoclusters, with some of the records not belonging to any of the clusters,in accordance with example embodiments.

FIG. 12C depicts the records of FIG. 12A, having been grouped intoclusters, with some of the records not belonging to any of the clusters,and a set of additional records, in accordance with example embodiments.

FIG. 12D depicts the records of FIG. 12C, having been grouped intoclusters, with some of the records not belonging to any of the clusters,in accordance with example embodiments.

FIG. 13 is a flow chart, in accordance with example embodiments.

FIG. 14 is a flow chart, in accordance with example embodiments.

DETAILED DESCRIPTION

Example methods, devices, and systems are described herein. It should beunderstood that the words “example” and “exemplary” are used herein tomean “serving as an example, instance, or illustration.” Any embodimentor feature described herein as being an “example” or “exemplary” is notnecessarily to be construed as preferred or advantageous over otherembodiments or features unless stated as such. Thus, other embodimentscan be utilized and other changes can be made without departing from thescope of the subject matter presented herein.

Accordingly, the example embodiments described herein are not meant tobe limiting. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations. For example, theseparation of features into “client” and “server” components may occurin a number of ways.

Further, unless context suggests otherwise, the features illustrated ineach of the figures may be used in combination with one another. Thus,the figures should be generally viewed as component aspects of one ormore overall embodiments, with the understanding that not allillustrated features are necessary for each embodiment.

Additionally, any enumeration of elements, blocks, or steps in thisspecification or the claims is for purposes of clarity. Thus, suchenumeration should not be interpreted to require or imply that theseelements, blocks, or steps adhere to a particular arrangement or arecarried out in a particular order.

I. Introduction

A large enterprise is a complex entity with many interrelatedoperations. Some of these are found across the enterprise, such as humanresources (HR), supply chain, information technology (IT), and finance.However, each enterprise also has its own unique operations that provideessential capabilities and/or create competitive advantages.

To support widely-implemented operations, enterprises typically useoff-the-shelf software applications, such as customer relationshipmanagement (CRM) and human capital management (HCM) packages. However,they may also need custom software applications to meet their own uniquerequirements. A large enterprise often has dozens or hundreds of thesecustom software applications. Nonetheless, the advantages provided bythe embodiments herein are not limited to large enterprises and may beapplicable to an enterprise, or any other type of organization, of anysize.

Many such software applications are developed by individual departmentswithin the enterprise. These range from simple spreadsheets tocustom-built software tools and databases. But the proliferation ofsiloed custom software applications has numerous disadvantages. Itnegatively impacts an enterprise's ability to run and grow itsoperations, innovate, and meet regulatory requirements. The enterprisemay find it difficult to integrate, streamline and enhance itsoperations due to lack of a single system that unifies its subsystemsand data.

To efficiently create custom applications, enterprises would benefitfrom a remotely-hosted application platform that eliminates unnecessarydevelopment complexity. The goal of such a platform would be to reducetime-consuming, repetitive application development tasks so thatsoftware engineers and individuals in other roles can focus ondeveloping unique, high-value features.

In order to achieve this goal, the concept of Application Platform as aService (aPaaS) is introduced, to intelligently automate workflowsthroughout the enterprise. An aPaaS system is hosted remotely from theenterprise, but may access data, applications, and services within theenterprise by way of secure connections. Such an aPaaS system may have anumber of advantageous capabilities and characteristics. Theseadvantages and characteristics may be able to improve the enterprise'soperations and workflow for IT, HR, CRM, customer service, applicationdevelopment, and security.

The aPaaS system may support development and execution ofmodel-view-controller (MVC) applications. MVC applications divide theirfunctionality into three interconnected parts (model, view, andcontroller) in order to isolate representations of information from themanner in which the information is presented to the user, therebyallowing for efficient code reuse and parallel development. Theseapplications may be web-based, and offer create, read, update, delete(CRUD) capabilities. This allows new applications to be built on acommon application infrastructure.

The aPaaS system may support standardized application components, suchas a standardized set of widgets for graphical user interface (GUI)development. In this way, applications built using the aPaaS system havea common look and feel. Other software components and modules may bestandardized as well. In some cases, this look and feel can be brandedor skinned with an enterprise's custom logos and/or color schemes.

The aPaaS system may support the ability to configure the behavior ofapplications using metadata. This allows application behaviors to berapidly adapted to meet specific needs. Such an approach reducesdevelopment time and increases flexibility. Further, the aPaaS systemmay support GUI tools that facilitate metadata creation and management,thus reducing errors in the metadata.

The aPaaS system may support clearly-defined interfaces betweenapplications, so that software developers can avoid unwantedinter-application dependencies. Thus, the aPaaS system may implement aservice layer in which persistent state information and other data arestored.

The aPaaS system may support a rich set of integration features so thatthe applications thereon can interact with legacy applications andthird-party applications. For instance, the aPaaS system may support acustom employee-onboarding system that integrates with legacy HR, IT,and accounting systems.

The aPaaS system may support enterprise-grade security. Furthermore,since the aPaaS system may be remotely hosted, it should also utilizesecurity procedures when it interacts with systems in the enterprise orthird-party networks and services hosted outside of the enterprise. Forexample, the aPaaS system may be configured to share data amongst theenterprise and other parties to detect and identify common securitythreats.

Other features, functionality, and advantages of an aPaaS system mayexist. This description is for purpose of example and is not intended tobe limiting.

As an example of the aPaaS development process, a software developer maybe tasked to create a new application using the aPaaS system. First, thedeveloper may define the data model, which specifies the types of datathat the application uses and the relationships therebetween. Then, viaa GUI of the aPaaS system, the developer enters (e.g., uploads) the datamodel. The aPaaS system automatically creates all of the correspondingdatabase tables, fields, and relationships, which can then be accessedvia an object-oriented services layer.

In addition, the aPaaS system can also build a fully-functional MVCapplication with client-side interfaces and server-side CRUD logic. Thisgenerated application may serve as the basis of further development forthe user. Advantageously, the developer does not have to spend a largeamount of time on basic application functionality. Further, since theapplication may be web-based, it can be accessed from anyInternet-enabled client device. Alternatively or additionally, a localcopy of the application may be able to be accessed, for instance, whenInternet service is not available.

The aPaaS system may also support a rich set of pre-definedfunctionality that can be added to applications. These features includesupport for searching, email, templating, workflow design, reporting,analytics, social media, scripting, mobile-friendly output, andcustomized GUIs.

The following embodiments describe architectural and functional aspectsof example aPaaS systems, as well as the features and advantagesthereof.

II. Example Computing Devices and Cloud-Based Computing Environments

FIG. 1 is a simplified block diagram exemplifying a computing device100, illustrating some of the components that could be included in acomputing device arranged to operate in accordance with the embodimentsherein. Computing device 100 could be a client device (e.g., a deviceactively operated by a user), a server device (e.g., a device thatprovides computational services to client devices), or some other typeof computational platform. Some server devices may operate as clientdevices from time to time in order to perform particular operations, andsome client devices may incorporate server features.

In this example, computing device 100 includes processor 102, memory104, network interface 106, and an input/output unit 108, all of whichmay be coupled by a system bus 110 or a similar mechanism. In someembodiments, computing device 100 may include other components and/orperipheral devices (e.g., detachable storage, printers, and so on).

Processor 102 may be one or more of any type of computer processingelement, such as a central processing unit (CPU), a co-processor (e.g.,a mathematics, graphics, or encryption co-processor), a digital signalprocessor (DSP), a network processor, and/or a form of integratedcircuit or controller that performs processor operations. In some cases,processor 102 may be one or more single-core processors. In other cases,processor 102 may be one or more multi-core processors with multipleindependent processing units. Processor 102 may also include registermemory for temporarily storing instructions being executed and relateddata, as well as cache memory for temporarily storing recently-usedinstructions and data.

Memory 104 may be any form of computer-usable memory, including but notlimited to random access memory (RAM), read-only memory (ROM), andnon-volatile memory (e.g., flash memory, hard disk drives, solid statedrives, compact discs (CDs), digital video discs (DVDs), and/or tapestorage). Thus, memory 104 represents both main memory units, as well aslong-term storage. Other types of memory may include biological memory.

Memory 104 may store program instructions and/or data on which programinstructions may operate. By way of example, memory 104 may store theseprogram instructions on a non-transitory, computer-readable medium, suchthat the instructions are executable by processor 102 to carry out anyof the methods, processes, or operations disclosed in this specificationor the accompanying drawings.

As shown in FIG. 1 , memory 104 may include firmware 104A, kernel 104B,and/or applications 104C. Firmware 104A may be program code used to bootor otherwise initiate some or all of computing device 100. Kernel 104Bmay be an operating system, including modules for memory management,scheduling and management of processes, input/output, and communication.Kernel 104B may also include device drivers that allow the operatingsystem to communicate with the hardware modules (e.g., memory units,networking interfaces, ports, and busses), of computing device 100.Applications 104C may be one or more user-space software programs, suchas web browsers or email clients, as well as any software libraries usedby these programs. Memory 104 may also store data used by these andother programs and applications.

Network interface 106 may take the form of one or more wirelineinterfaces, such as Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, andso on). Network interface 106 may also support communication over one ormore non-Ethernet media, such as coaxial cables or power lines, or overwide-area media, such as Synchronous Optical Networking (SONET) ordigital subscriber line (DSL) technologies. Network interface 106 mayadditionally take the form of one or more wireless interfaces, such asIEEE 802.11 (Wifi), BLUETOOTH®, global positioning system (GPS), or awide-area wireless interface. However, other forms of physical layerinterfaces and other types of standard or proprietary communicationprotocols may be used over network interface 106. Furthermore, networkinterface 106 may comprise multiple physical interfaces. For instance,some embodiments of computing device 100 may include Ethernet,BLUETOOTH®, and Wifi interfaces.

Input/output unit 108 may facilitate user and peripheral deviceinteraction with computing device 100. Input/output unit 108 may includeone or more types of input devices, such as a keyboard, a mouse, a touchscreen, and so on. Similarly, input/output unit 108 may include one ormore types of output devices, such as a screen, monitor, printer, and/orone or more light emitting diodes (LEDs). Additionally or alternatively,computing device 100 may communicate with other devices using auniversal serial bus (USB) or high-definition multimedia interface(HDMI) port interface, for example.

In some embodiments, one or more computing devices like computing device100 may be deployed to support an aPaaS architecture. The exact physicallocation, connectivity, and configuration of these computing devices maybe unknown and/or unimportant to client devices. Accordingly, thecomputing devices may be referred to as “cloud-based” devices that maybe housed at various remote data center locations.

FIG. 2 depicts a cloud-based server cluster 200 in accordance withexample embodiments. In FIG. 2 , operations of a computing device (e.g.,computing device 100) may be distributed between server devices 202,data storage 204, and routers 206, all of which may be connected bylocal cluster network 208. The number of server devices 202, datastorages 204, and routers 206 in server cluster 200 may depend on thecomputing task(s) and/or applications assigned to server cluster 200.

For example, server devices 202 can be configured to perform variouscomputing tasks of computing device 100. Thus, computing tasks can bedistributed among one or more of server devices 202. To the extent thatthese computing tasks can be performed in parallel, such a distributionof tasks may reduce the total time to complete these tasks and return aresult. For purpose of simplicity, both server cluster 200 andindividual server devices 202 may be referred to as a “server device.”This nomenclature should be understood to imply that one or moredistinct server devices, data storage devices, and cluster routers maybe involved in server device operations.

Data storage 204 may be data storage arrays that include drive arraycontrollers configured to manage read and write access to groups of harddisk drives and/or solid state drives. The drive array controllers,alone or in conjunction with server devices 202, may also be configuredto manage backup or redundant copies of the data stored in data storage204 to protect against drive failures or other types of failures thatprevent one or more of server devices 202 from accessing units of datastorage 204. Other types of memory aside from drives may be used.

Routers 206 may include networking equipment configured to provideinternal and external communications for server cluster 200. Forexample, routers 206 may include one or more packet-switching and/orrouting devices (including switches and/or gateways) configured toprovide (i) network communications between server devices 202 and datastorage 204 via local cluster network 208, and/or (ii) networkcommunications between the server cluster 200 and other devices viacommunication link 210 to network 212.

Additionally, the configuration of routers 206 can be based at least inpart on the data communication requirements of server devices 202 anddata storage 204, the latency and throughput of the local clusternetwork 208, the latency, throughput, and cost of communication link210, and/or other factors that may contribute to the cost, speed,fault-tolerance, resiliency, efficiency and/or other design goals of thesystem architecture.

As a possible example, data storage 204 may include any form ofdatabase, such as a structured query language (SQL) database. Varioustypes of data structures may store the information in such a database,including but not limited to tables, arrays, lists, trees, and tuples.Furthermore, any databases in data storage 204 may be monolithic ordistributed across multiple physical devices.

Server devices 202 may be configured to transmit data to and receivedata from data storage 204. This transmission and retrieval may take theform of SQL queries or other types of database queries, and the outputof such queries, respectively. Additional text, images, video, and/oraudio may be included as well. Furthermore, server devices 202 mayorganize the received data into web page representations. Such arepresentation may take the form of a markup language, such as thehypertext markup language (HTML), the extensible markup language (XML),or some other standardized or proprietary format. Moreover, serverdevices 202 may have the capability of executing various types ofcomputerized scripting languages, such as but not limited to Perl,Python, PHP Hypertext Preprocessor (PHP), Active Server Pages (ASP),JAVASCRIPT®, and so on. Computer program code written in these languagesmay facilitate the providing of web pages to client devices, as well asclient device interaction with the web pages.

III. Example Remote Network Management Architecture

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments. This architecture includes three maincomponents, managed network 300, remote network management platform 320,and third-party networks 340, all connected by way of Internet 350.

Managed network 300 may be, for example, an enterprise network used byan entity for computing and communications tasks, as well as storage ofdata. Thus, managed network 300 may include client devices 302, serverdevices 304, routers 306, virtual machines 308, firewall 310, and/orproxy servers 312. Client devices 302 may be embodied by computingdevice 100, server devices 304 may be embodied by computing device 100or server cluster 200, and routers 306 may be any type of router,switch, or gateway.

Virtual machines 308 may be embodied by one or more of computing device100 or server cluster 200. In general, a virtual machine is an emulationof a computing system, and mimics the functionality (e.g., processor,memory, and communication resources) of a physical computer. Onephysical computing system, such as server cluster 200, may support up tothousands of individual virtual machines. In some embodiments, virtualmachines 308 may be managed by a centralized server device orapplication that facilitates allocation of physical computing resourcesto individual virtual machines, as well as performance and errorreporting. Enterprises often employ virtual machines in order toallocate computing resources in an efficient, as needed fashion.Providers of virtualized computing systems include VMWARE® andMICROSOFT®.

Firewall 310 may be one or more specialized routers or server devicesthat protect managed network 300 from unauthorized attempts to accessthe devices, applications, and services therein, while allowingauthorized communication that is initiated from managed network 300.Firewall 310 may also provide intrusion detection, web filtering, virusscanning, application-layer gateways, and other applications orservices. In some embodiments not shown in FIG. 3 , managed network 300may include one or more virtual private network (VPN) gateways withwhich it communicates with remote network management platform 320 (seebelow).

Managed network 300 may also include one or more proxy servers 312. Anembodiment of proxy servers 312 may be a server device that facilitatescommunication and movement of data between managed network 300, remotenetwork management platform 320, and third-party networks 340. Inparticular, proxy servers 312 may be able to establish and maintainsecure communication sessions with one or more computational instancesof remote network management platform 320. By way of such a session,remote network management platform 320 may be able to discover andmanage aspects of the architecture and configuration of managed network300 and its components. Possibly with the assistance of proxy servers312, remote network management platform 320 may also be able to discoverand manage aspects of third-party networks 340 that are used by managednetwork 300.

Firewalls, such as firewall 310, typically deny all communicationsessions that are incoming by way of Internet 350, unless such a sessionwas ultimately initiated from behind the firewall (i.e., from a deviceon managed network 300) or the firewall has been explicitly configuredto support the session. By placing proxy servers 312 behind firewall 310(e.g., within managed network 300 and protected by firewall 310), proxyservers 312 may be able to initiate these communication sessions throughfirewall 310. Thus, firewall 310 might not have to be specificallyconfigured to support incoming sessions from remote network managementplatform 320, thereby avoiding potential security risks to managednetwork 300.

In some cases, managed network 300 may consist of a few devices and asmall number of networks. In other deployments, managed network 300 mayspan multiple physical locations and include hundreds of networks andhundreds of thousands of devices. Thus, the architecture depicted inFIG. 3 is capable of scaling up or down by orders of magnitude.

Furthermore, depending on the size, architecture, and connectivity ofmanaged network 300, a varying number of proxy servers 312 may bedeployed therein. For example, each one of proxy servers 312 may beresponsible for communicating with remote network management platform320 regarding a portion of managed network 300. Alternatively oradditionally, sets of two or more proxy servers may be assigned to sucha portion of managed network 300 for purposes of load balancing,redundancy, and/or high availability.

Remote network management platform 320 is a hosted environment thatprovides aPaaS services to users, particularly to the operators ofmanaged network 300. These services may take the form of web-basedportals, for instance. Thus, a user can securely access remote networkmanagement platform 320 from, for instance, client devices 302, orpotentially from a client device outside of managed network 300. By wayof the web-based portals, users may design, test, and deployapplications, generate reports, view analytics, and perform other tasks.

As shown in FIG. 3 , remote network management platform 320 includesfour computational instances 322, 324, 326, and 328. Each of theseinstances may represent one or more server devices and/or one or moredatabases that provide a set of web portals, services, and applications(e.g., a wholly-functioning aPaaS system) available to a particularcustomer. In some cases, a single customer may use multiplecomputational instances. For example, managed network 300 may be anenterprise customer of remote network management platform 320, and mayuse computational instances 322, 324, and 326. The reason for providingmultiple instances to one customer is that the customer may wish toindependently develop, test, and deploy its applications and services.Thus, computational instance 322 may be dedicated to applicationdevelopment related to managed network 300, computational instance 324may be dedicated to testing these applications, and computationalinstance 326 may be dedicated to the live operation of testedapplications and services. A computational instance may also be referredto as a hosted instance, a remote instance, a customer instance, or bysome other designation. Any application deployed onto a computationalinstance may be a scoped application, in that its access to databaseswithin the computational instance can be restricted to certain elementstherein (e.g., one or more particular database tables or particular rowswith one or more database tables).

For purpose of clarity, the disclosure herein refers to the physicalhardware, software, and arrangement thereof as a “computationalinstance.” Note that users may colloquially refer to the graphical userinterfaces provided thereby as “instances.” But unless it is definedotherwise herein, a “computational instance” is a computing systemdisposed within remote network management platform 320.

The multi-instance architecture of remote network management platform320 is in contrast to conventional multi-tenant architectures, overwhich multi-instance architectures exhibit several advantages. Inmulti-tenant architectures, data from different customers (e.g.,enterprises) are comingled in a single database. While these customers'data are separate from one another, the separation is enforced by thesoftware that operates the single database. As a consequence, a securitybreach in this system may impact all customers' data, creatingadditional risk, especially for entities subject to governmental,healthcare, and/or financial regulation. Furthermore, any databaseoperations that impact one customer will likely impact all customerssharing that database. Thus, if there is an outage due to hardware orsoftware errors, this outage affects all such customers. Likewise, ifthe database is to be upgraded to meet the needs of one customer, itwill be unavailable to all customers during the upgrade process. Often,such maintenance windows will be long, due to the size of the shareddatabase.

In contrast, the multi-instance architecture provides each customer withits own database in a dedicated computing instance. This preventscomingling of customer data, and allows each instance to beindependently managed. For example, when one customer's instanceexperiences an outage due to errors or an upgrade, other computationalinstances are not impacted. Maintenance down time is limited because thedatabase only contains one customer's data. Further, the simpler designof the multi-instance architecture allows redundant copies of eachcustomer database and instance to be deployed in a geographicallydiverse fashion. This facilitates high availability, where the liveversion of the customer's instance can be moved when faults are detectedor maintenance is being performed.

In some embodiments, remote network management platform 320 may includeone or more central instances, controlled by the entity that operatesthis platform. Like a computational instance, a central instance mayinclude some number of physical or virtual servers and database devices.Such a central instance may serve as a repository for data that can beshared amongst at least some of the computational instances. Forinstance, definitions of common security threats that could occur on thecomputational instances, software packages that are commonly discoveredon the computational instances, and/or an application store forapplications that can be deployed to the computational instances mayreside in a central instance. Computational instances may communicatewith central instances by way of well-defined interfaces in order toobtain this data.

In order to support multiple computational instances in an efficientfashion, remote network management platform 320 may implement aplurality of these instances on a single hardware platform. For example,when the aPaaS system is implemented on a server cluster such as servercluster 200, it may operate a virtual machine that dedicates varyingamounts of computational, storage, and communication resources toinstances. But full virtualization of server cluster 200 might not benecessary, and other mechanisms may be used to separate instances. Insome examples, each instance may have a dedicated account and one ormore dedicated databases on server cluster 200. Alternatively,computational instance 322 may span multiple physical devices.

In some cases, a single server cluster of remote network managementplatform 320 may support multiple independent enterprises. Furthermore,as described below, remote network management platform 320 may includemultiple server clusters deployed in geographically diverse data centersin order to facilitate load balancing, redundancy, and/or highavailability.

Third-party networks 340 may be remote server devices (e.g., a pluralityof server clusters such as server cluster 200) that can be used foroutsourced computational, data storage, communication, and servicehosting operations. These servers may be virtualized (i.e., the serversmay be virtual machines). Examples of third-party networks 340 mayinclude AMAZON WEB SERVICES® and MICROSOFT® AZURE®. Like remote networkmanagement platform 320, multiple server clusters supporting third-partynetworks 340 may be deployed at geographically diverse locations forpurposes of load balancing, redundancy, and/or high availability.

Managed network 300 may use one or more of third-party networks 340 todeploy applications and services to its clients and customers. Forinstance, if managed network 300 provides online music streamingservices, third-party networks 340 may store the music files and provideweb interface and streaming capabilities. In this way, the enterprise ofmanaged network 300 does not have to build and maintain its own serversfor these operations.

Remote network management platform 320 may include modules thatintegrate with third-party networks 340 to expose virtual machines andmanaged services therein to managed network 300. The modules may allowusers to request virtual resources and provide flexible reporting forthird-party networks 340. In order to establish this functionality, auser from managed network 300 might first establish an account withthird-party networks 340, and request a set of associated resources.Then, the user may enter the account information into the appropriatemodules of remote network management platform 320. These modules maythen automatically discover the manageable resources in the account, andalso provide reports related to usage, performance, and billing.

Internet 350 may represent a portion of the global Internet. However,Internet 350 may alternatively represent a different type of network,such as a private wide-area or local-area packet-switched network.

FIG. 4 further illustrates the communication environment between managednetwork 300 and computational instance 322, and introduces additionalfeatures and alternative embodiments. In FIG. 4 , computational instance322 is replicated across data centers 400A and 400B. These data centersmay be geographically distant from one another, perhaps in differentcities or different countries. Each data center includes supportequipment that facilitates communication with managed network 300, aswell as remote users.

In data center 400A, network traffic to and from external devices flowseither through VPN gateway 402A or firewall 404A. VPN gateway 402A maybe peered with VPN gateway 412 of managed network 300 by way of asecurity protocol such as Internet Protocol Security (IPSEC) orTransport Layer Security (TLS). Firewall 404A may be configured to allowaccess from authorized users, such as user 414 and remote user 416, andto deny access to unauthorized users. By way of firewall 404A, theseusers may access computational instance 322, and possibly othercomputational instances. Load balancer 406A may be used to distributetraffic amongst one or more physical or virtual server devices that hostcomputational instance 322. Load balancer 406A may simplify user accessby hiding the internal configuration of data center 400A, (e.g.,computational instance 322) from client devices. For instance, ifcomputational instance 322 includes multiple physical or virtualcomputing devices that share access to multiple databases, load balancer406A may distribute network traffic and processing tasks across thesecomputing devices and databases so that no one computing device ordatabase is significantly busier than the others. In some embodiments,computational instance 322 may include VPN gateway 402A, firewall 404A,and load balancer 406A.

Data center 400B may include its own versions of the components in datacenter 400A. Thus, VPN gateway 402B, firewall 404B, and load balancer406B may perform the same or similar operations as VPN gateway 402A,firewall 404A, and load balancer 406A, respectively. Further, by way ofreal-time or near-real-time database replication and/or otheroperations, computational instance 322 may exist simultaneously in datacenters 400A and 400B.

Data centers 400A and 400B as shown in FIG. 4 may facilitate redundancyand high availability. In the configuration of FIG. 4 , data center 400Ais active and data center 400B is passive. Thus, data center 400A isserving all traffic to and from managed network 300, while the versionof computational instance 322 in data center 400B is being updated innear-real-time. Other configurations, such as one in which both datacenters are active, may be supported.

Should data center 400A fail in some fashion or otherwise becomeunavailable to users, data center 400B can take over as the active datacenter. For example, domain name system (DNS) servers that associate adomain name of computational instance 322 with one or more InternetProtocol (IP) addresses of data center 400A may re-associate the domainname with one or more IP addresses of data center 400B. After thisre-association completes (which may take less than one second or severalseconds), users may access computational instance 322 by way of datacenter 400B.

FIG. 4 also illustrates a possible configuration of managed network 300.As noted above, proxy servers 312 and user 414 may access computationalinstance 322 through firewall 310. Proxy servers 312 may also accessconfiguration items 410. In FIG. 4 , configuration items 410 may referto any or all of client devices 302, server devices 304, routers 306,and virtual machines 308, any applications or services executingthereon, as well as relationships between devices, applications, andservices. Thus, the term “configuration items” may be shorthand for anyphysical or virtual device, or any application or service remotelydiscoverable or managed by computational instance 322, or relationshipsbetween discovered devices, applications, and services. Configurationitems may be represented in a configuration management database (CMDB)of computational instance 322.

As noted above, VPN gateway 412 may provide a dedicated VPN to VPNgateway 402A. Such a VPN may be helpful when there is a significantamount of traffic between managed network 300 and computational instance322, or security policies otherwise suggest or require use of a VPNbetween these sites. In some embodiments, any device in managed network300 and/or computational instance 322 that directly communicates via theVPN is assigned a public IP address. Other devices in managed network300 and/or computational instance 322 may be assigned private IPaddresses (e.g., IP addresses selected from the 10.0.0.0-10.255.255.255or 192.168.0.0-192.168.255.255 ranges, represented in shorthand assubnets 10.0.0.0/8 and 192.168.0.0/16, respectively).

IV. Example Device, Application, and Service Discovery

In order for remote network management platform 320 to administer thedevices, applications, and services of managed network 300, remotenetwork management platform 320 may first determine what devices arepresent in managed network 300, the configurations and operationalstatuses of these devices, and the applications and services provided bythe devices, and well as the relationships between discovered devices,applications, and services. As noted above, each device, application,service, and relationship may be referred to as a configuration item.The process of defining configuration items within managed network 300is referred to as discovery, and may be facilitated at least in part byproxy servers 312.

For purpose of the embodiments herein, an “application” may refer to oneor more processes, threads, programs, client modules, server modules, orany other software that executes on a device or group of devices. A“service” may refer to a high-level capability provided by multipleapplications executing on one or more devices working in conjunctionwith one another. For example, a high-level web service may involvemultiple web application server threads executing on one device andaccessing information from a database application that executes onanother device.

FIG. 5A provides a logical depiction of how configuration items can bediscovered, as well as how information related to discoveredconfiguration items can be stored. For sake of simplicity, remotenetwork management platform 320, third-party networks 340, and Internet350 are not shown.

In FIG. 5A, CMDB 500 and task list 502 are stored within computationalinstance 322. Computational instance 322 may transmit discovery commandsto proxy servers 312. In response, proxy servers 312 may transmit probesto various devices, applications, and services in managed network 300.These devices, applications, and services may transmit responses toproxy servers 312, and proxy servers 312 may then provide informationregarding discovered configuration items to CMDB 500 for storagetherein. Configuration items stored in CMDB 500 represent theenvironment of managed network 300.

Task list 502 represents a list of activities that proxy servers 312 areto perform on behalf of computational instance 322. As discovery takesplace, task list 502 is populated. Proxy servers 312 repeatedly querytask list 502, obtain the next task therein, and perform this task untiltask list 502 is empty or another stopping condition has been reached.

To facilitate discovery, proxy servers 312 may be configured withinformation regarding one or more subnets in managed network 300 thatare reachable by way of proxy servers 312. For instance, proxy servers312 may be given the IP address range 192.168.0/24 as a subnet. Then,computational instance 322 may store this information in CMDB 500 andplace tasks in task list 502 for discovery of devices at each of theseaddresses.

FIG. 5A also depicts devices, applications, and services in managednetwork 300 as configuration items 504, 506, 508, 510, and 512. As notedabove, these configuration items represent a set of physical and/orvirtual devices (e.g., client devices, server devices, routers, orvirtual machines), applications executing thereon (e.g., web servers,email servers, databases, or storage arrays), relationshipstherebetween, as well as services that involve multiple individualconfiguration items.

Placing the tasks in task list 502 may trigger or otherwise cause proxyservers 312 to begin discovery. Alternatively or additionally, discoverymay be manually triggered or automatically triggered based on triggeringevents (e.g., discovery may automatically begin once per day at aparticular time).

In general, discovery may proceed in four logical phases: scanning,classification, identification, and exploration. Each phase of discoveryinvolves various types of probe messages being transmitted by proxyservers 312 to one or more devices in managed network 300. The responsesto these probes may be received and processed by proxy servers 312, andrepresentations thereof may be transmitted to CMDB 500. Thus, each phasecan result in more configuration items being discovered and stored inCMDB 500.

In the scanning phase, proxy servers 312 may probe each IP address inthe specified range of IP addresses for open Transmission ControlProtocol (TCP) and/or User Datagram Protocol (UDP) ports to determinethe general type of device. The presence of such open ports at an IPaddress may indicate that a particular application is operating on thedevice that is assigned the IP address, which in turn may identify theoperating system used by the device. For example, if TCP port 135 isopen, then the device is likely executing a WINDOWS® operating system.Similarly, if TCP port 22 is open, then the device is likely executing aUNIX® operating system, such as LINUX®. If UDP port 161 is open, thenthe device may be able to be further identified through the SimpleNetwork Management Protocol (SNMP). Other possibilities exist. Once thepresence of a device at a particular IP address and its open ports havebeen discovered, these configuration items are saved in CMDB 500.

In the classification phase, proxy servers 312 may further probe eachdiscovered device to determine the version of its operating system. Theprobes used for a particular device are based on information gatheredabout the devices during the scanning phase. For example, if a device isfound with TCP port 22 open, a set of UNIX®-specific probes may be used.Likewise, if a device is found with TCP port 135 open, a set ofWINDOWS®-specific probes may be used. For either case, an appropriateset of tasks may be placed in task list 502 for proxy servers 312 tocarry out. These tasks may result in proxy servers 312 logging on, orotherwise accessing information from the particular device. Forinstance, if TCP port 22 is open, proxy servers 312 may be instructed toinitiate a Secure Shell (SSH) connection to the particular device andobtain information about the operating system thereon from particularlocations in the file system. Based on this information, the operatingsystem may be determined. As an example, a UNIX® device with TCP port 22open may be classified as AIX®, HPUX, LINUX®, MACOS®, or SOLARIS®. Thisclassification information may be stored as one or more configurationitems in CMDB 500.

In the identification phase, proxy servers 312 may determine specificdetails about a classified device. The probes used during this phase maybe based on information gathered about the particular devices during theclassification phase. For example, if a device was classified as LINUX®,a set of LINUX®-specific probes may be used. Likewise, if a device wasclassified as WINDOWS® 2012, as a set of WINDOWS®-2012-specific probesmay be used. As was the case for the classification phase, anappropriate set of tasks may be placed in task list 502 for proxyservers 312 to carry out. These tasks may result in proxy servers 312reading information from the particular device, such as basicinput/output system (BIOS) information, serial numbers, networkinterface information, media access control address(es) assigned tothese network interface(s), IP address(es) used by the particular deviceand so on. This identification information may be stored as one or moreconfiguration items in CMDB 500.

In the exploration phase, proxy servers 312 may determine furtherdetails about the operational state of a classified device. The probesused during this phase may be based on information gathered about theparticular devices during the classification phase and/or theidentification phase. Again, an appropriate set of tasks may be placedin task list 502 for proxy servers 312 to carry out. These tasks mayresult in proxy servers 312 reading additional information from theparticular device, such as processor information, memory information,lists of running processes (applications), and so on. Once more, thediscovered information may be stored as one or more configuration itemsin CMDB 500.

Running discovery on a network device, such as a router, may utilizeSNMP. Instead of or in addition to determining a list of runningprocesses or other application-related information, discovery maydetermine additional subnets known to the router and the operationalstate of the router's network interfaces (e.g., active, inactive, queuelength, number of packets dropped, etc.). The IP addresses of theadditional subnets may be candidates for further discovery procedures.Thus, discovery may progress iteratively or recursively.

Once discovery completes, a snapshot representation of each discovereddevice, application, and service is available in CMDB 500. For example,after discovery, operating system version, hardware configuration andnetwork configuration details for client devices, server devices, androuters in managed network 300, as well as applications executingthereon, may be stored. This collected information may be presented to auser in various ways to allow the user to view the hardware compositionand operational status of devices, as well as the characteristics ofservices that span multiple devices and applications.

Furthermore, CMDB 500 may include entries regarding dependencies andrelationships between configuration items. More specifically, anapplication that is executing on a particular server device, as well asthe services that rely on this application, may be represented as suchin CMDB 500. For instance, suppose that a database application isexecuting on a server device, and that this database application is usedby a new employee onboarding service as well as a payroll service. Thus,if the server device is taken out of operation for maintenance, it isclear that the employee onboarding service and payroll service will beimpacted. Likewise, the dependencies and relationships betweenconfiguration items may be able to represent the services impacted whena particular router fails.

In general, dependencies and relationships between configuration itemsmay be displayed on a web-based interface and represented in ahierarchical fashion. Thus, adding, changing, or removing suchdependencies and relationships may be accomplished by way of thisinterface.

Furthermore, users from managed network 300 may develop workflows thatallow certain coordinated activities to take place across multiplediscovered devices. For instance, an IT workflow might allow the user tochange the common administrator password to all discovered LINUX®devices in a single operation.

In order for discovery to take place in the manner described above,proxy servers 312, CMDB 500, and/or one or more credential stores may beconfigured with credentials for one or more of the devices to bediscovered. Credentials may include any type of information needed inorder to access the devices. These may include userid/password pairs,certificates, and so on. In some embodiments, these credentials may bestored in encrypted fields of CMDB 500. Proxy servers 312 may containthe decryption key for the credentials so that proxy servers 312 can usethese credentials to log on to or otherwise access devices beingdiscovered.

The discovery process is depicted as a flow chart in FIG. 5B. At block520, the task list in the computational instance is populated, forinstance, with a range of IP addresses. At block 522, the scanning phasetakes place. Thus, the proxy servers probe the IP addresses for devicesusing these IP addresses, and attempt to determine the operating systemsthat are executing on these devices. At block 524, the classificationphase takes place. The proxy servers attempt to determine the operatingsystem version of the discovered devices. At block 526, theidentification phase takes place. The proxy servers attempt to determinethe hardware and/or software configuration of the discovered devices. Atblock 528, the exploration phase takes place. The proxy servers attemptto determine the operational state and applications executing on thediscovered devices. At block 530, further editing of the configurationitems representing the discovered devices and applications may takeplace. This editing may be automated and/or manual in nature.

The blocks represented in FIG. 5B are for purpose of example. Discoverymay be a highly configurable procedure that can have more or fewerphases, and the operations of each phase may vary. In some cases, one ormore phases may be customized, or may otherwise deviate from theexemplary descriptions above.

V. Natural Language Processing of Text Queries

Natural language processing is a discipline that involves, among otheractivities, using computers to understand the structure and meaning ofhuman language. This determined structure and meaning may be applicableto the processing of IT incidents, as described below.

Each incident may be represented as an incident report. While incidentreports may exist in various formats and contain various types ofinformation, an example incident report 600 is shown in FIG. 6 .Incident report 600 consists of a number of fields in the left column,at least some of which are associated with values in the right column.

Field 602 identifies the originator of the incident, in this case BobSmith. Field 604 identifies the time at which the incident was created,in this case 9:56 AM on Feb. 7, 2018. Field 605 is a text string thatprovides a short description of the problem. Field 606 identifies thedescription of the problem, as provided by the originator. Thus, field606 may be a free-form text string containing anywhere from a few wordsto several sentences or more. Field 608 is a categorization of theincident, in this case email. This categorization may be provided by theoriginator, the IT personnel to whom the incident is assigned, orautomatically based on the context of the problem description field.

Field 610 identifies the IT personnel to whom the incident is assigned(if applicable), in this case Alice Jones. Field 612 identifies thestatus of the incident. The status may be one of “open,” “assigned,”“working,” or “resolved” for instance. Field 614 identifies how theincident was resolved (if applicable). This field may be filled out bythe IT personnel to whom the incident is assigned or another individual.Field 616 identifies the time at which the incident was resolved, inthis case 10:10 AM on Feb. 7, 2018. Field 618 specifies the closure codeof the incident (if applicable) and can take on values such as “closed(permanently)”, “closed (work around)”, “closed (cannot reproduce)”,etc. Field 620 identifies any additional notes added to the record, suchas by the IT personnel to whom the incident is assigned. Field 622identifies a link to an online article that may help users avoid havingto address a similar issue in the future.

Incident report 600 is presented for purpose of example. Other types ofincident reports may be used, and these reports may contain more, fewer,and/or different fields.

Incident reports, such as incident report 600, may be created in variousways. For instance, by way of a web form, an email sent to a designatedaddress, a voicemail box using speech-to-text conversion, and so on.These incident reports may be stored in an incident report database thatcan be queried. As an example, a query in the form of a text stringcould return one or more incident reports that contain the words in thetext string. Additionally or alternatively, one or more elements of anincident report (e.g., a “short description” field) may be used to querya database of knowledgebase articles, other incident reports, or someother corpus of text. This may be done in order to identify otherincident reports, resolved past incident reports, reports on theresolution of past problems, knowledgebase articles, or otherinformation that may be relevant to the incident report in order tofacilitate resolution of a problem represented in the incident report.

This process is illustrated in FIG. 7 . A text query may be entered intoweb interface 700. This web interface may be supplied by way of acomputational instance of remote network management platform 320. Webinterface 700 converts the text query into a database query (e.g., anSQL query), and provides the SQL query to database 702. This databasemay be CMDB 500 or some other database. Database 702 contains a numberof incident reports with problem description fields as shown in FIG. 6 .Regardless, database 702 conducts the query and returns matching resultsto web interface 700. One or more such results may be returned. Webinterface 700 provides these results as a web page.

For example, if the text query is “email”, web interface 700 may convertthis query into an SQL query of database 702. For example, the query maylook at the problem description field of a table containing incidentreports. Any such incident report that matches the query—i.e., includesthe term “email”—may be provided in the query results. Thus, theincident reports with the problem descriptions of “My email client isnot downloading new emails”, “Email crashed”, and “Can't connect toemail” may be provided, while the incident report with the problemdescription “VPN timed out” is not returned.

This matching technique is simplistic and has a number of drawbacks. Itonly considers the presence of the text of the query in the incidents.Thus, it does not consider contextual information, such as wordsappearing before and after the query text. Also, synonyms of the querytext (e.g., “mail” or “message”) and misspellings of the query text(e.g., “email”) would not return any results in this example.

Furthermore, deploying such a solution would involve use of aninefficient sparse matrix, with entries in one dimension for each wordin the English language and entries in the other dimension for theproblem description of each incident. While the exact number of Englishwords is a matter of debate, there are at least 150,000-200,000, withless than about 20,000 in common use. Given that a busy IT departmentcan have a database of tens of thousands of incidents, this matrix wouldbe quite large and wasteful to store even if just the 20,000 mostcommonly used words are included.

Thus, the above methods of comparison may be replaced by and/oraugmented with a variety of methods that compare the semantic contentand/or context of text samples. These methods can improve a variety ofmachine learning techniques to facilitate natural language processing.Such techniques can include determining word and/or paragraph vectorsfrom samples of text, applying artificial neural networks or other deeplearning algorithms, sentiment analysis, or other techniques in order todetermine a similarity between samples of text. For example, these orother natural language processing techniques can be applied to determinethe similarity between one or more text fields of an incident report andother incident reports, resolved incident reports, knowledgebasearticles, or other potentially relevant samples of text.

VI. Natural Language Processing of Text Queries Based on SemanticContent

The degree of similarity between two samples of text can be determinedin a variety of ways. The two samples of text could be a text field ofan incident report and a text field of another incident report, a textfield of a resolved incident report, a knowledgebase article, or someother sample of text that may be relevant to the resolution,classification, or other aspects of an incident report. Additionally oralternatively, one or both of the samples could be segments of textwithin a larger sample of text. As noted above, a degree of overlapbetween the identities of words present in the two samples of textand/or a word matrix method could be used to determine the degree ofsimilarity. Additionally or alternatively, one or more techniques ofnatural language processing could be applied to compare the samples oftext such that the context or other semantic content of the textsaffects the determined similarity value between the samples of text.

Such techniques may be applied to improve text query matching related toincident reports. These techniques may include a variety of machinelearning algorithms that can be trained based on samples of text. Thesamples of text used for training can include past examples of incidentreports, knowledgebase articles, or other text samples of the samenature as the text samples to which the trained model will be applied.This has the benefit of providing a model that has been uniquely adaptedto the vocabulary, topics, and idiomatic word use common in its intendedapplication.

Such techniques can include determining word and/or paragraph vectorsfrom samples of text, applying ANNs or other deep learning algorithms,performing sentiment analysis, or other techniques in order to determinea similarity between samples of text, to group multiple samples of texttogether according to topic or content, to partition a sample of textinto discrete internally-related segments, to determine statisticalassociations between words, or to perform some other language processingtask. Below, a particular method for determining similarity valuesbetween samples of text using an ANN model that provides compactsemantic representations of words and text strings is provided as anon-limiting example of such techniques. However, other techniques maybe applied to generate similarity values between samples of text asapplied elsewhere herein. In the discussion below, there are twoapproaches for training an ANN model to represent the sematic meaningsof words: word vectors and paragraph vectors. These techniques may becombined with one another or with other techniques.

A. Word Vectors

A “word vector” may be determined for each word present in a corpus oftext records such that words having similar meanings (or “semanticcontent”) are associated with word vectors that are near each otherwithin a semantically encoded vector space. Such vectors may havedozens, hundreds, or more elements. These word vectors allow theunderlying meaning of words to be compared or otherwise operated on by acomputing device. Accordingly, the use of word vectors may allow for asignificant improvement over simpler word list or word matrix methods.

Word vectors can be used to quickly and efficiently compare the overallsemantic content of samples of text, allowing a similarity value betweenthe samples of text to be determined. This can include determining adistance, a cosine similarity, or some other measure of similaritybetween the word vectors of the words in each of the text samples. Forexample, a mean of the word vectors in each of the text samples could bedetermined and a cosine similarity between the means then used as ameasure of similarity between the text samples. Additionally oralternatively, the word vectors may be provided as input to an ANN, asupport vector machine, a decision tree, or some other machine learningalgorithm in order to perform sentiment analysis, to classify or clustersamples of text, to determine a level of similarity between samples oftext, or to perform some other language processing task.

Word vectors may be determined for a set of words in a variety of ways.In an example, a matrix of the word vectors can be an input layer of anANN. The ANN (including the matrix of word vectors) can then be trainedwith a large number of text strings from a database to determine thecontextual relationships between words appearing in these text strings.Such an ANN 800 is shown in FIG. 8A. ANN 800 includes input layer 802,which feeds into hidden layer 804, which in turn feeds into output layer806. The number of nodes in input layer 802 and output layer 806 may beequivalent to the number of words in a pre-defined vocabulary ordictionary (e.g., 20,000, 50,000, or 100,000). The number of nodes inhidden layer 804 may be much smaller (e.g., 64 as shown in FIG. 8A, orother values such as 16, 32, 128, 512, 1024, etc.).

For each text string in the database, ANN 800 is trained with one ormore arrangements of words. For instance, in FIG. 8B, ANN 800 is shownbeing trained with input word “email” and output (context) words“can't”, “connect” and “to”. The output words serve as the ground truthoutput values to which the results produced by output layer 806 arecompared. This arrangement reflects that “email” appears proximate to“can't”, “connect” and “to” in a text string in database 702.

In an implementation, this could be represented as node I₂ receiving aninput of 1, and all other nodes in input layer 802 receiving an input of0. Similarly, node O₁ is associated with a ground truth value of“can't”, node O₂ is associated with a ground truth value of “connect”,and node O₃ is associated with a ground truth value of “to”. In theimplementation, this could be represented as nodes O₁, O₂, and O₃ beingassociated with ground truth values of 1 and all other nodes in outputlayer 806 being associated with ground truth values of 0. The lossfunction may be a sum of squared errors, for example, between theoutputs generated by output layer 806 in response to the input describedabove and a vector containing the ground truth values associated withthe output layer nodes.

Other arrangements of this text string from database 702 may be used totrain an ANN. For instance, as shown in Figure A the input word to inputlayer 902 of an ANN 900 may be “can't” and the output words of outputlayer 906 may be “connect”, “to”, and “email.” In another example, asshown in FIG. 9B, the input word may be “connect” and the output wordsmay be “can't”, “to”, and “email.”

In general, these arrangements may be selected so that the output wordsare within w words of the input word (e.g., where w could be 1, 2, 3, 5,etc.), the output words are in the same sentence as the input word, theoutput words are in the same paragraph as the input word, and so on.Furthermore, various word arrangements of each text string in database702 may be used to train ANN (e.g., ANN 800 or ANN 900). These textstrings may be selected from short description field 605, problemdescription field 606, category field 608, resolution field 614, notesfield 620, and/or any other field or combination of fields in anincident report.

After an ANN (e.g., 800, 900) is trained with these arrangements of textstrings, the hidden layer (e.g., 804, 904) of the ANN becomes a compactvector representation of the context and meaning of an input word. Thatis, the weightings from a particular node (e.g., I₃) in the input layerto the hidden layer represent the elements of the word vector of theword corresponding to the particular node (e.g., “can't”). For example,assuming that ANN is fully-trained with a corpus of 10,000 or so textstrings (though more or fewer text strings may be used), an input wordof “email” may have a similar vector representation of an input word of“mail”. Intuitively, since hidden layer is all that ANN has to determinethe context of an input word, if two words have similar contexts, thenthey are highly likely to have similar vector representations.

In some embodiments, an ANN can be trained with input words associatedwith the output nodes O₁ . . . O_(n) and the output (context) wordsassociated with input nodes I₁ . . . I_(n). This arrangement may producean identical or similar vector for hidden layer.

Furthermore, vectors generated in this fashion are additive. Thus,subtracting the vector representation of “mail” from the vectorrepresentation of “email” is expected to produce a vector with valuesclose to 0. However, subtracting the vector representation of “VPN” fromthe vector representation of “email” is expected to produce a vectorwith higher values. In this manner, the model indicates that “email” and“mail” have closer meanings than “email” and “VPN”.

Once vector representations have been determined for all words ofinterest, linear and/or multiplicative aggregations of these vectors maybe used to represent text strings. For instance, a vector for the textstring “can't connect to email” can be found by adding together theindividual vectors for the words “can't”, “connect”, “to”, and “email”.In some cases, an average or some other operation may be applied to thevectors for the words. This can be expressed below as the vector sum ofm vectors v_(i) with each entry therein divided by m, where i={1 . . .m}. But other possibilities, such as weighted averages, exist.

$\begin{matrix}{v_{avg} = {\frac{1}{m}{\sum\limits_{i = 1}^{m}v_{i}}}} & (1)\end{matrix}$

Regardless of how the aggregations are determined, this generaltechnique allows vector representations for each text string in database702 to be found. These vector representations may be stored in database702 as well, either along with their associated text strings orseparately. These vector representations can then be used to compare thetext strings, cluster or group the text strings, train some othermachine learning classifier, or to perform some other task. For example,a matching text string for a particularly query text may be determinedby determining a cosine similarity or other similarity value between thevector representation of the query text and the stored vectorrepresentations of samples of text in the database 702.

The comparison may identify one or more text string vectors fromdatabase 702 that “match” in this fashion. In some cases this may be thek text string vectors with the highest similarity, or any text stringvector with a similarity that is greater than a pre-determined value.The identified text string vectors could correspond to a subset ofincident reports, within a greater corpus of incident reports that isrecorded in the database 702, that are relevant to an additionalincident report that corresponds to the query text string vector. Foreach of the identified text string vectors, the associated text stringmay be looked up in database 702 and provided as an output text string.In some cases, the associated incident reports may be provided as well.

In some cases, only incident reports that are not older than apre-determined age are provided. For instance, the system may beconfigured to identify text string vectors only from incident reportsthat were resolved within the last 3 months, 6 months, or 12 months.Alternatively, the system may be configured to identify text stringvectors only from incident reports that were opened within the last 3months, 6 months, or 12 months.

In this fashion, incident reports with similar problem descriptions asthat of the input text string can be rapidly identified. Notably, thissystem provides contextual results that are more likely to be relevantand meaningful to the input text string. Consequently, an individual canreview these incident reports to determine how similar problems as thatin the problem description have been reported and addressed in the past.This may result in the amount of time it takes to resolve incidentsbeing dramatically reduced.

Additionally or alternatively, these embodiments can be applied todetect and identify clusters of semantically and/or contextually similarincident reports within a corpus of incident reports. For example,clusters of incident reports related to a similar issue that is likelyto affect users of an IT system, an ongoing misconfiguration of one ormore aspects of an IT system, a progressive hardware failure in acomponent of an IT system, or some other recurring issue within an ITsystem. Identifying such clusters of related incident reports can allowthe IT system to be repaired or upgraded (e.g., by replacing and/orreconfiguring failing or inconsistently performing hardware orsoftware), users to be trained to avoid common mistakes,rarely-occurring hardware or software issues to be detected andrectified, or other benefits.

Such clusters of relevant incident reports can be detected and/oridentified by identifying, within the semantically encoded vector space,aggregated word (and/or paragraph) vectors corresponding to the incidentreports. A variety of methods could be employed to detect such clusterswithin the semantically encoded vector space, e.g., k-means clustering,support vector machines, ANNs (e.g., unsupervised ANNs configured and/ortrained to identify relevant subsets of training examples within acorpus of available training examples), or some other classifier orother method for identifying clusters of related vectors within a vectorspace.

B. Paragraph Vectors

As discussed previously, an ANN model (e.g., 800, 900) uses thesurrounding context to provide compact, semantically relevant vectorrepresentations of words. After training, words with similar meaningscan map to a similar position in the vector space. For example, thevectors for “powerful” and “strong” may appear close to each other,whereas the vectors for “powerful” and “Paris” may be farther apart.Additions and subtractions between word vectors also carry meaning.Using vector algebra on the determined word vectors, we can answeranalogy questions such as “King”−“man”+“woman”=“Queen.”

However, the complete semantic meaning of a sentence or other passage(e.g., a phrase, several sentences, a paragraph, a text segment within alarger sample of text, or a document) cannot always be captured from theindividual word vectors of a sentence (e.g., by applying vectoralgebra). Word vectors can represent the semantic content of individualwords and may be trained using short context windows. Thus, the semanticcontent of word order and any information outside the short contextwindow is lost when operating based only on word vectors.

Take for example the sentence “I want a big green cell right now.” Inthis case, simple vector algebra of the individual words may fail toprovide the correct semantic meaning of the word “cell,” as the word“cell” has multiple possible meanings and thus can be ambiguous.Depending on the context, “cell” could be a biological cell, a prisoncell, or a cell of a cellular communications network. Accordingly, theparagraph, sentence, or phrase from which a given word is sampled canprovide crucial contextual information.

In another example, given the sentence “Where art thou ______,” it iseasy to predict the missing word as “Romeo” if sentence was said toderive from a paragraph about Shakespeare. Thus, learning a semanticvector representation of an entire paragraph can help contribute topredicting the context of words sampled from that paragraph.

Similar to the methods above for learning word vectors, an ANN or othermachine learning structures may be trained using a large number ofparagraphs in a corpus to determine the contextual meaning of entireparagraphs, sentences, phrases, or other multi-word text samples as wellas to determine the meaning of the individual words that make up theparagraphs in the corpus. Such an ANN 1000 is shown in FIG. 10A. ANN1000 includes input layer 1002, which feeds into hidden layer 1004,which in turn feeds into output layer 1006. Note that input layer 1002consists of two types of input substructures, the top substructure 1008(consisting of input nodes I₁ . . . I_(n)) representing words and thebottom substructure 1010 (consisting of input nodes D₁ . . . D_(m))representing paragraphs (documents). The number of nodes in output layer1006 and the top input layer substructure 1008 may be equal to thenumber of unique words in the entire corpus. The number of nodes in thebottom input layer substructure 1010 may be equivalent to the number ofunique paragraphs in the entire corpus. Note that “paragraph,” as usedherein, may be a sentence, a paragraph, one or more fields of anincident report, a segment of a larger string of text, or some othermulti-word string of text.

For each paragraph in the corpus, ANN 1000 is trained with fixed-lengthcontexts generated from moving a sliding window over the paragraph.Thus, a given paragraph vector is shared across all training contextscreated from its source paragraph, but not across training contextscreated from other paragraphs. Word vectors are shared across trainingcontexts created from all paragraphs, e.g., the vector for “cannot” isthe same for all paragraphs. Paragraphs are not limited in size; theycan be as large as entire documents or as small as a sentence or phrase.In FIG. 10A, ANN 1000 is shown in a single training iteration, beingtrained with input word context “can't,” “connect” and “to,” inputparagraph context DOC 1, and output word “email.” The output word servesas the ground truth output value to which the result produced by outputlayer 1006 is compared. This arrangement reflects that “email” appearsproximate to “can't”, “connect”, and “to”, and is within DOC 1.

In an implementation, this could be represented as output node O₄receiving a ground truth value of 1 and all other nodes in output layer1006 having ground truth values of 0. Similarly, node I₁ has a groundtruth value of “can't,” node I₂ has a ground truth value of “connect,”node I₃ has a ground truth value of “to,” and node D₁ has ground truthvalue of DOC 1. In the implementation, this could be represented asnodes I₁, I₂, I₃, and D₁ being associated with values of 1 and all othernodes in input layer 1002 having values of 0. The loss function may be asum of squared errors, for example, between the output of output layer1006 and a vector containing the ground truth values. The weight valuesof the corresponding word vectors and paragraph vectors, as well all theoutput layer parameters (e.g., softmax weights) are updated based on theloss function (e.g., via backpropagation).

FIG. 10B shows ANN 1000 being trained with a subsequent context window.This context window derives from the same document, but shifts ahead aword in the document and uses input word context “connect,” “to” and“email,” input paragraph context DOC 1, and output word “server.” In animplementation, these inputs and outputs can be encoded with groundtruth values as similarly described above.

FIG. 10C shows an instance of ANN 1000 trained with another documentwithin the corpus. The context window derives from this document anduses input word context “can't”, “load”, and “my”, input paragraphcontext DOC 2, and output word “database.” In an implementation, theseinputs and outputs can be encoded with ground truth values as similarlydescribed above.

After ANN 1000 is trained, the weights associated with hidden layer 1004become a compact vector representation of the context and meaning ofinput words and paragraphs. For example, assuming that ANN 1000 isfully-trained with a corpus of 1,000 paragraphs, with the entire corpuscontaining 10,000 unique words, each paragraph and each word can berepresented by a unique vector with a length equal to the number ofhidden nodes in hidden layer 1004. These unique vectors encode thecontextual meaning of words within the paragraphs or the paragraphsthemselves.

FIG. 10D shows ANN 1000 at prediction time performing an inference stepto compute the paragraph vector for a new, previously unseen paragraph.This inference step begins by adding an additional input node 1012 toinput layer substructure 1010 that represents the unseen paragraph (DOCM+1). During this inference process, the coefficients of the wordvectors substructure 1008 and the learned weights between hidden layer1004 and output layer 1006 are held fixed. Thus, the model generates anadditional paragraph vector 1012, corresponding to the unseen paragraphin the input paragraph vector substructure 1010, to obtain the newsemantic vector representation of the unseen paragraph. Any additionalunseen paragraphs can be trained through a similar process by addinginput nodes to input layer substructure 1010.

Alternatively, paragraph vectors can be trained by ignoring word contextin the input layer, only using the paragraph vector as the input, andforcing the model to predict different word contexts randomly sampledfrom the paragraph in the output layer. The input layer of such an ANNonly consists of paragraph vectors, while the output layer represents asingle context window that is randomly generated from a given paragraph.Training such an ANN may result in a vector representation for thesemantic content of paragraphs in the corpus, but will not necessarilyprovide any semantic vector representations for the words therein.

Once vector representations have been determined for paragraphs in thecorpus, linear and/or multiplicative aggregation of these vectors may beused to represent topics of interest. Furthermore, if the dimensions ofparagraph vectors are the same as the dimensions of word vectors, asshown in ANN 1000, then linear and multiplicative aggregation betweenword vectors and paragraphs vectors can be obtained. For example,finding the Chinese equivalent of “Julius Caesar” using an encyclopediaas a corpus can be achieved by vector operations PV(“JuliusCaesar”)−WV(“Roman”)+WV(“Chinese”), where PV is a paragraph vector(representing an entire Wikipedia article) and WV are word vectors.Thus, paragraph vectors can achieve the same kind of analogies to wordvectors with more context-based results.

In practice, such learned paragraph vectors can be used as inputs intoother supervised learning models, such as sentiment prediction models.In such models, which can include but are not limited to ANNs, SupportVector Machines (SVMs), or Naïve Bayes Classifiers, paragraph vectorsare used as input with a corresponding sentiment label as output. Othermetrics such as cosine similarity and nearest neighbors clusteringalgorithms can be applied to paragraph vectors to find or groupparagraphs on similar topics within the corpus of paragraphs.

In the present embodiments, a combination of learned word vectors andparagraph vectors can help determine the structure and meaning ofincidents reports, for example incident report 600 as shown in FIG. 6 .Incident report 600 consists of a number of fields in the left column,at least some of which are associated with values in the right column.For longer text fields, such as short description field 605, problemdescription field 606, resolution field 614, and notes field 620, it maybe preferable to represent the associated right column text as aparagraph vector, or as multiple paragraph vectors corresponding torespective text segments within the right column text, to gain morecontextual meaning rather than aggregating the individual word vectorsthat form the text. Incident report 600 is presented for purpose ofexample. Various fields of an incident report can be arranged to berepresented as paragraph vectors, word vectors, or weighted combinationsof the two. Other types of incident reports, problem reports, casefiles, or knowledgebase articles may also be used, and these reports maycontain more, fewer, and/or different fields.

After representing different fields as paragraph vectors, word vectors,or weighted combinations of the two, a single vector to represent theentire incident can be generated by concatenating, generating a vectorsum, or otherwise aggregating the word and/or paragraph vectorrepresentations of the individual incident fields. With a singleaggregate incident vector representation, a system can be configured toidentify similar aggregate vectors (and therefore similar incidentreports) based on cosine similarity or other metrics as discussed above.Alternatively, a search for similar incident reports may use just theparagraph text of one or more individual fields. In this fashion, textfrom one or more individual fields in an incident report could becombined into a single paragraph of text. A paragraph vector could thenbe generated from this single, large paragraph of concatenated text andused to search for similar incidents.

This process can be illustrated in terms of the previously described ANNstructures. Initially, text strings are obtained from database 702 ofFIG. 7 . As noted above, these text strings may be from parts ofincident reports. Then, words are extracted from the text strings. Thewords extracted may be all of the words in the text strings or some ofthese words. These extracted words are provided as input to ANN 900 ofFIGS. 9A-9B. The substring contexts of these words are extracted fromthe text strings. The substring contexts may be one or more substringscontaining words before, after, or surrounding the associated words thatwere extracted. These vector representations may then be used to compare(e.g., using cosine similarity) their respective text samples.

The comparison may identify one or more incident reports from database702 that “match” in this fashion. In some cases this may be the kincident reports with the highest similarity, or any incident reportwith a similarity that is greater than a pre-determined value. The usermay be provided with these identified incident reports or referencesthereto.

In some cases, only incident reports that are not older than apre-determined age are provided. For instance, the system may beconfigured to only identify incident reports that were resolved withinthe last 3 months, 6 months, or 12 months. Alternatively, the system maybe configured to only identify incident reports that were opened withinthe last 3 months, 6 months, or 12 months.

In this fashion, incident reports with similar content as that of theinput incident report can be rapidly identified. Consequently, anindividual can review these incident reports to determine how similarproblems as that in the incident have been reported and addressed in thepast. This may result in the amount of time it takes to resolveincidents being dramatically reduced.

While this section describes some possible embodiments of word vectorsand paragraph vectors, other embodiments may exist. For example,different ANN structures and different training procedures can be used.

VII. Clustering of Text Queries or Other Records

Queries, incident reports, knowledgebase articles, and/or other textualor non-textual records can be clustered together. Such clustering may beperformed to provide a variety of benefits. For example, clustering maybe applied to a set of records in order to identify patterns or groupswithin the set of records that have relevance to the operation of asystem or organization. Such groups may facilitate the tracking ofongoing problems (e.g., network outages, user confusion interfacing witha network-based service) by measuring a time-dependence of recordsassigned to a particular cluster associated with the ongoing problem(s).Such groups may facilitate the early identification of newly-emergingproblems by, e.g., identifying similarities between newly-receivedreports. In some examples, clustering may allow similar reports (e.g.,reports corresponding to the same cluster(s)) to be manipulated incommon, in order to reduce the time required to respond to sets ofsimilar reports. For example, reports that are related to a networkoutage and that are assigned to a single cluster could all be resolvedin a single operation following resolution of the network outage.

In some examples, clustering may facilitate the allocation of reports totechnicians according to specialty, familiarity, or other factors.Additionally or alternatively, a knowledgebase article, solutionflowchart, or other material could be created for each identifiedcluster in order to facilitate resolution of reports as they areassigned to the clusters. Identifying clusters within a set of queries,incident reports, or other textual or non-textual records can provideadditional or alternative benefits.

Incident reports, queries, knowledgebase articles, or types of recordsthat may include textual elements and/or non-textual elements can begrouped into clusters in a variety of ways. Such clustering may beperformed in a supervised manner in order to generate a clusteringalgorithm that assigns novel records into clusters according to amanually-classified set of training records. Additionally oralternatively, clustering may be performed in an unsupervised manner inorder to generate clusters without the requirement of manually-labeledrecords, to identify previously un-identified clusters within thetraining data, or to provide some other benefit.

A variety of methods and/or machine learning algorithms could be appliedto identify clusters within a set of records and/or to assign records(e.g., newly received or generated records) to already-identifiedclusters. For example, decision trees, artificial neural networks,k-means, support vector machines, independent component analysis,principal component analysis, or some other method could be trainedbased on a set of available records in order to generate an ML model toclassify the available records and/or to classify records not present inthe training set of available records. The inputs to such an ML modelcould include a variety of features of the records. Such features couldbe present in the records (e.g., dates and times, status flags, userIDs) and/or determined from information already present in the records(e.g., word vectors, paragraph vectors). The input features couldinclude dates and times or other numerical information related to therecords. The input features could include categorical information likeuser ID numbers or status flags (e.g., ‘open,’ ‘closed-resolved,’‘closed-unresolved’). The input features could include informationrelated to textual information (e.g., a ‘problem description’ field) ofthe records. For example, the input features could be related to wordand/or paragraph vectors generated from textual fields of the recordsand/or other features generated using natural language processing. Theinput features could be subjected to a mapping (e.g., a nonlineartransformation, a dimensionality reduction) prior to being applied tothe ML model.

FIG. 11A illustrates an example set of records 1100. For the purposes ofillustrating clustering of the records, each record is represented by arespective location within a two-dimensional space. The location of agiven record within the space could be related to the value of twofeatures of the given record (e.g., a time of generation of the recordand a time of resolution of the record, two dimensions of a paragraphvector generated from text of the record). Alternatively, the locationof a given record could be related to a projection of more than twofeatures of the record into the two-dimensional space using a linear ornonlinear dimensionality reduction technique or some other mappingmethod.

Similarity values can be determined for pairs of records in the set ofrecords 1100. Such similarity values could be used to generate an MLmodel to cluster the records 1100 such that that records that are‘similar’ in some sense are assigned to the same cluster, while recordsthat are very ‘dissimilar’ are assigned to different clusters. Such asimilarity value could correspond to a distance measure between therecords in some space, e.g., the two-dimensional space of FIG. 11A, asemantically-encoded vector space related to word and/or paragraphvectors determined from textual aspects of the records, a vector spacethat includes dimensions relating to the time of generation of therecord or other numerical and/or categorical information of the record,etc. Such a distance could be a Euclidean distance, a Manhattandistance, or some other distance measure according to an application.

FIG. 11B shows each record in the set of records 1100 assigned to one ofthree clusters 1110A, 1120A, 1130A identified from the set of records1100. The assignment of each record is indicated by the shape used toindicate the record; a filled circle for the first cluster 1110A, anopen circle for the second cluster 1120A, and a square for the thirdcluster 1130A. Note that all of the records have been assigned to acluster; in practice, an ML model used to assign records to clusterscould optionally assign records to a residual set of records or takesome other action instead of assigning a record to a cluster. This couldbe done, e.g., to avoid assigning records to clusters when thesimilarity between the record and any potential cluster is less than aminimum degree of similarity.

As noted above, a variety of methods could be used to generate an MLmodel that assigns records to two or more clusters and/or that assignsrecords to a set of residual, un-assigned records. Once the ML model hasbeen determined, the ML model can be applied to assign additionalrecords to the identified clusters represented by the ML model and/or toassign records to a set of residual records. The ML model could includeparameter values, neural network hyperparameters, cluster centroidlocations in feature space, cluster boundary locations in feature space,threshold similarity values, or other information used, by the ML model,to determine which cluster to assign a record and/or to determine thatthe record should not be assigned to a cluster (e.g., should be storedin a set of residual, un-assigned records). Such information coulddefine a region, within a feature space, that corresponds to eachcluster. That is, the information in the ML model could be such that theML model assigns a record to a particular cluster if the features of therecord correspond to a location, within the feature space, that isinside the defined region for the particular cluster. The definedregions could be closed (being fully enclosed by a boundary) or open(having one or more boundaries but extending infinitely outward in oneor more directions in the feature space).

FIG. 11C illustrates such regions 1110B, 1120B, 1130B in the exampletwo-dimensional feature space of FIG. 11A. Records of the set of records1100 fall within the regions 1110B, 1120B, 1130B and thus would beassigned, by an ML model, to clusters that correspond to the regions1110B, 1120B, 1130B. Records having locations outside of the regions1110B, 1120B, 1130B could be assigned to a set of residual records. TheML model could include information indicative of the location, shape,extent, or other information about splines, hyperplanes, cells,hyperspheres, or other shapes expressly defining the boundaries and/orextent of the regions 1110B, 1120B, 1130B, within a feature space, thatcorrespond to the clusters represented by the ML model. Additionally oralternatively, the ML model could include network weight parameters,output unit scaling parameters, or other information about a neuralnetwork structure or other algorithm that indirectly define theboundaries and/or extent of the regions 1110B, 1120B, 1130B.

In some examples, the ML model could include centroids or other locationinformation indicative of the location, within a feature space, of theclusters. A centroid could be a location of an arithmetic or geometricmean of the locations of records in the cluster, a determined geometriccenter or other defining location of a hypersphere, hyperellipsoid, orother shape fitted to the records of the cluster, or some other locationrelated to the overall location and/or extent of the cluster in afeature space. In such examples, a record could be assigned to aparticular cluster when the location of the record, in the featurespace, is closer to the centroid of the particular cluster than it is tothe centroid of any other cluster. An ML model organized in such amanner could include a k-means classifier. As an illustrative example,FIG. 11D includes three centroids 1115C, 1125C, 1135C in a featurespace. Each centroid corresponds to a respective cluster. Records fromthe set of records 1100 can be assigned to the clusters based on whichof the centroids 1115C, 1125C, 1135C is nearest to the location of therecord in the feature space. Under such an arrangement, records will beassigned to clusters based on which of the open regions 1110C, 1120C,1130C the location of the record is within.

In some examples, a record could be precluded from assignment to aparticular cluster unless a degree of similarity between the cluster andthe record is greater than a threshold similarity. This could include adistance between the location of the record and a centroid or othercharacteristic location of the cluster being less than a thresholddistance. Records that are precluded from inclusion in any cluster couldbe added to a set of residual records. As an illustrative example, FIG.11E includes three centroids 1115D, 1125D, 1135D in a feature space.Each centroid corresponds to a respective cluster. Records from the setof records 1100 can be assigned to the clusters based on which of thecentroids 1115D, 1125D, 1135D is nearest to the location of the recordin the feature space and less than a threshold distance d away from thelocation of the record. Under such an arrangement, records will beassigned to clusters based on which of the closed regions 1110D, 1120D,1130D the location of the record is within. Note that the thresholddistances could vary between the clusters. For example, the thresholddistance could be based on a variance of the records assigned to eachcluster during training of the ML model.

In some examples, the ML model could operate in a specified order todetermine whether a record should be assigned to each cluster. Forexample, the ML model could first determine whether a record should beassigned to a first cluster (e.g., by comparing a distance between acentroid of the first cluster and a location of the record to athreshold distance). If it is determined that the record should not beassigned to the first record, the ML model could operate to determinewhether the record should be assigned to a second cluster, and so on.Such a method could have the benefit of reducing the expectedcomputational cost of assigning a record to a cluster (or determiningthat the record should not be assigned to any cluster). Additionally oralternatively, such a method could allow additional clusters to be addedto the model without re-assigning any regions of the feature space tothe new cluster that had formerly been associated with any of thepre-existing clusters. This could be done by placing the newly addedcluster(s) to the end of the sequence for determining whether the recordshould be assigned to any of the clusters.

As an illustrative example, FIG. 11F includes three centroids 1115E,1125E, 1135E in a feature space. Each centroid corresponds to arespective cluster. Records from the set of records 1100 can be assignedto the clusters by determining, according to a specified sequence of theclusters, whether the location of the record in the feature space isless than a threshold distance away from the location of the centroid ofthe cluster(s). Under such an arrangement, records will be assigned toclusters based on which of the closed regions 1110E, 1120E, 1130E thelocation of the record is within. Note that the threshold distances varybetween the clusters.

The clustering and ML model training operations described above could bedistributed amongst elements of a remote network management platform asdescribed elsewhere herein. For example, an end-user computationalinstance within the remote network management platform could bededicated to the management of a particular user and configured to serveweb pages, manage the generation and storage of incident reports,maintain and serve a set of knowledgebase articles, manage servers of amanaged network, or to perform some other operations related to themaintenance and operation of a managed network of the user.

Additional sets of computational resources could be allocated togenerate and/or apply ML models based on incident reports, knowledgebasearticles, or other textual records related to the end-user computationalinstance and/or to the managed network. This distribution of tasks canallow ML-related tasks, which can be memory and compute time intensive,to be performed separately from other network management tasks (e.g.,serving web pages, managing servers, generating incident reports) sothat the performance of those other network management tasks is notdegraded when generating or applying an ML model. Additionally, thisdistribution of tasks could allow resources used to generate and applyML models to be shared between different users of the remote networkmanagement platform to increase efficiency.

In some examples, a training computational instance could generate theML model initially. This could be done in response to a request for suchfrom the end-user computational instance. The training instance can thenaccess a set of incident reports or other records related to theend-user computational instance (e.g., by receiving such informationfrom the end-user computational instance) and use that information togenerate the ML model. The generated ML model could then be stored bythe training instance for later use, transmitted to the end-userinstance for storage/later use, and/or stored in some other location forlater use.

Additional incident reports or other records could then be assigned intoclusters and/or added to a set of residual records using the trained MLmodel. This could be done by the end-user instance. Alternatively, aprediction computational instance within the remote network managementplatform could receive a request for such classification from theend-user instance. Such a request could include an indication of theincident report or record to be classified. The prediction instancecould then apply the ML model to assign the incident report or otherrecord to a cluster or to the set of residual records. The predictioninstance could access the ML model from a local memory or from adatabase that is not part of the prediction instance. For example, theprediction instance could determine that the relevant ML model is notpresent in a memory (e.g., hard drive, database) of the predictioninstance and, responsive to that determination, the prediction instancecould transmit, to the end-use instance, a request for a copy of the MLmodel. Once the prediction instance has assigned the incident report orother record to a particular cluster, the prediction instance cantransmit, to the end-user instance, a representation of the particularcluster.

Transmitting a representation of one or more clusters of textual recordswithin a corpus of textual records can include transmitting a variety ofinformation related to the represented cluster(s). This can includetransmitting an identification number (e.g., a GUID), a location withina database, or some other identifying information that can enable theend-user instance to identify the represented cluster of textualrecords. Additionally or alternatively, a representation of the contentsof the cluster of textual records could be transmitted. This couldinclude transmitting identification information for the textual recordswithin the cluster (e.g., GUIDs, locations within a database), all orpart of the contents of the textual records within the cluster (e.g.,text from ‘problem resolution’ fields of incident reports within thecluster), the contents of one or more representative textual recordsassociated with the cluster (e.g., a knowledgebase article associatedwith the cluster, a ‘problem resolution’ field or other contents of anincident report that is near a centroid of the cluster), or some othercontent associated with the cluster.

Transmitting a representation of one or more textual records in a corpusof textual records can include transmitting a variety of differentinformation. In some examples, transmitting a representation of atextual record includes transmitting an identification number (e.g., aglobal unique identifier (GUID)), time and date stamp, a location withina database, or some other identifying information that can enable theend-user instance to identify the represented textual record in adatabase or to otherwise access the represented textual record.Additionally or alternatively, transmitting a representation of atextual record can include transmitting a copy of the textual recorditself or a portion thereof. For example, transmitting a representationof a textual record can include transmitting a copy of a ‘problemresolution’ field of an incident report.

VIII. Clustering of Text Queries or Other Records

Generating an ML model to identify related clusters of incident reports(or other records) within a training set of incident reports, and usingthat ML model to subsequently assign newly-generated incident reports tothe identified clusters, can provide a variety of benefits. However, theprocess of initially generating the ML model can be computationallyexpensive. Accordingly, the ML model may be used for an extended periodof time following its initial generation. However, as new incidentreports are generated over time, the effectiveness of the ML model maybe reduced. This could be related to the presence of novel clusters ofrelated incident reports within the newly-generated incident reports,changes in the properties over time of the identified clusters (e.g.,movement through a feature space), or other processes. Accordingly, theML model may be re-generated, based in whole or in part on thenewly-generated incident reports, according to a specified schedule orsome other criteria.

The accuracy of the ML model between these updates may be furtherincreased by performing partial updates based on residual incidentreports that have not been assigned to the clusters already identifiedin the ML model. Such residual incident reports could have been added tothe set of residual records due to being less similar to all of theidentified clusters than a threshold similarity. For example, theresidual incident reports could all be more than a maximum distance, ina feature space, from the centroid or other location of any of theidentified clusters. Accordingly, these residual records may representun-identified clusters in the data and can be periodically analyzed,between full re-generations of the ML model, to determine whetheradditional clusters should be added to the ML model. This solution hasthe benefit of being less computationally expensive than a fullre-generation of the ML model while allowing the ML model to detectemerging clusters in the newly-generated incident reports.

FIG. 12A illustrates an example set of records 1200. For the purposes ofillustrating clustering of the records, each record is represented by arespective location within a two-dimensional space. Similarity valuescan be determined for pairs of records in the set of records 1200 andused to generate an ML model to cluster the records 1200 such that thatrecords that are ‘similar’ in some sense are assigned to the samecluster, while records that are very ‘dissimilar’ are assigned todifferent clusters.

FIG. 12B shows each record in the set of records 1200 and a set of threeclusters 1210, 1220, 1230 identified from the set of records 1200. Asubset of the records (indicated by the filled circles) have beenassigned into the clusters 1210, 1220, 1230. A disjoint subset of therecords 1200 have been added to a set of residual incident reports(indicated by the open circles) due, e.g., to those records being lesssimilar to any of the identified clusters 1210, 1220, 1230 than athreshold degree of similarity. As shown in FIG. 12B, such a thresholddegree of similarity could correspond to a maximum distance in a featurespace, and the threshold level of similarity may vary amongst theclusters.

Over time, additional incident reports can be generated. FIG. 12C showsthe original set of incident reports 1200 (indicated by closed or filledcircles), the identified clusters 1210, 1220, 1230, and a set ofnewly-generated incident reports (closed or filled squares). The MLmodel has been applied to these additional incident reports to assignsome of the additional incident reports (filled squares) tocorresponding clusters. Additional incident reports that have beendetermined not to correspond to any of the identified clusters 1210,1220, 1230 have been added to the set of residual incident reports (opensquares).

Prior to completely re-generating the ML model, the set of residualincident reports can be analyzed in order to identify additionalclusters that are not already present in the ML model. This analysis mayoccur according to a set schedule (e.g., every 15 minutes). Additionallyor alternatively, the analysis to identify additional clusters from theset of residual incident reports may occur in response to some othercriteria. For example, the analysis may occur in response to a specifiednumber of additional incident reports being added to the set of residualincident reports.

FIG. 12D shows an additional cluster 1240 that has been identified fromthe set of residual records (open circles/squares) shown in FIG. 12C.Incident reports from the set of residual incident reports thatcorrespond to the newly identified cluster 1240 have been removed fromthe set of residual incident reports and assigned to the newlyidentified cluster 1240. Note that the incident reports assigned to thenewly identified cluster 1240 can include both incident reports thatwere part of the original training set of incident reports as well asincident reports generated subsequently.

The algorithm used to identify the additional cluster(s) from the set ofresidual incident reports could be the same algorithm used to originallygenerate the ML model. Alternatively, a different method could beapplied. The ML model may be expanded to include the newly identifiedcluster(s). This expanded ML model could be transmitted to thecorresponding end-user computational instance (e.g., for distribution toother prediction and/or training instances, for the purpose ofprotecting the end-user's data) or to some other database for later useby a prediction instance or by some other system or component.

The above-described process can act to iteratively add clusters to an MLmodel based on a residual set of incident reports. As new clusters areidentified, incident reports that belong to the new clusters are removedfrom the set of residual incident reports. Accordingly, the order inwhich incident reports are applied to the ML model, and thus potentiallyadded to the set of residual incident reports, can have an effect on theclusters that are identified via this method. In order to ensure thatthe same set of clusters is identified by multiple different predictioninstances (or other instances performing these operations), the orderingof the incident reports can be recorded and made available to theprediction instances.

For example, a prediction instance could determine that it has anout-of-date version of the ML model. The version of the ML model couldbe out of date due to not representing all of the relevant incidentreports that had been generated. In response to determining that it'slocal version of the ML model is out-of-date, the prediction instancecould request, from an end-user instance, information about newlygenerated incident reports as well as the order in which those incidentreports were received. The prediction instance could then apply thenewly generated incident reports in order to update the out-of-dateversion of the ML model. Under such an update scheme, multiple differentprediction instances could independently generate identical updated MLmodels.

The above methods can be applied to update an ML model based on incidentreports that were not used to initially generate the ML model.Periodically, an ML model updated via these methods may be completelyre-generated. This may be done according to a specified schedule or inresponse to some other criteria (a ‘refresh criterion’) in order toprovide more accurate, up-to-date clusters or to provide some otherbenefit. In some instances, it may be beneficial to preserve, in somemanner, one or more of the identified clusters from the previous MLmodel. This could be done to preserve clusters that have become useful,that have been the focus of significant development work (e.g., tocreate knowledgebase articles, resolution workflows, or other efforts),that are related to an ongoing concern or event (e.g., a network orservice outage), or that are desirable in some other manner. In suchexamples, a user could specify the one or more clusters to be preserved,and some information related to the specified clusters could be used tore-generate the ML model.

Generating an ML model to preserve one or more specified clusters from aprevious model could include a variety of processes. In some examples, acorresponding cluster of the new ML model could be set according to aspecified cluster. This could include defining the corresponding clusterin the new ML to have the same centroid, maximum distance threshold, orother properties according to the corresponding properties of thespecified cluster from the previous ML model. Additionally oralternatively, an initial state or seed of one or more clusters in thenew ML model could be set to such information of the specified cluster,such that the corresponding cluster of the new ML model may differslightly from the specified cluster (e.g., to account for changes in theproperties of the underlying process related to the specified cluster).

In some examples, the new ML model could be generated such that some orall of the members of the specified cluster remain in the same cluster.This could include using a supervised model generation algorithm withthe members of the specified cluster tagged as having the same ‘true’output classification. The ML model generation algorithm could thenoperate based on these tags, as well as other information. This could bea hard requirement, requiring all of the members of the specifiedcluster to be assigned to a single cluster in common by the new MLmodel. Alternatively, this could be a soft requirement, allowing some ofthe members of the specified cluster to be separated amongst multipledifferent clusters in the new ML model.

IX. Example Operations

FIG. 13 is a flow chart illustrating an example embodiment. The processillustrated by FIG. 13 may be carried out by a computing device, such ascomputing device 100, and/or a cluster of computing devices, such asserver cluster 200. However, the process can be carried out by othertypes of devices or device subsystems. For example, the process could becarried out by a portable computer, such as a laptop or a tablet device.

The embodiments of FIG. 13 may be simplified by the removal of any oneor more of the features shown therein. Further, these embodiments may becombined with features, aspects, and/or implementations of any of theprevious figures or otherwise described herein.

The example method of FIG. 13 includes receiving, by a predictioncomputational instance and from an end-user computational instance, afirst textual record (1300). The end-user computational instance isdedicated to a managed network, and the prediction computationalinstance and the end-user computational instance are both disposedwithin a remote network management platform.

The example method of FIG. 13 additionally includes applying an MLpipeline to the target data set (1300). The example method of FIG. 13further includes determining, by an ML clustering model of theprediction computational instance that represents a set of clusters oftextual records, that the first textual record corresponds to aparticular cluster selected from the set of clusters of textual records(1302). The example method of FIG. 13 yet further includes transmitting,by the prediction computational instance and to the end-usercomputational instance, a representation of the particular cluster(1304).

The example method of FIG. 13 additionally includes receiving, by theprediction computational instance and from the end-user computationalinstance, a second textual record (1306). The example method of FIG. 13also includes determining, by the ML clustering model, that the secondtextual record does not correspond to any cluster of textual records ofthe set of clusters of textual records (1308). The example method ofFIG. 13 additionally includes adding, by the prediction computationalinstance, the second textual record to a stored set of residual textualrecords in response to determining that the second textual record doesnot correspond to any cluster of textual records of the set of clustersof textual records (1310).

The example method of FIG. 13 yet further includes identifying, by theprediction computational instance, an additional cluster of textualrecords based on the stored set of residual textual records (1312). Theexample method of FIG. 13 also includes transmitting, by the predictioncomputational instance and to the end-user computational instance, arepresentation of the additional cluster of textual records (1314).

The example method of FIG. 13 could include additional or alternativesteps. In some examples, the prediction computational instance is afirst prediction computational instance and the ML clustering model is afirst ML clustering model and the example method of FIG. 13 couldadditionally include: (i) receiving, by the first predictioncomputational instance and from the end-user computational instanceprior to receiving the first textual record, a first plurality oftextual records; (ii) prior to receiving the first textual record,using, by the first prediction computational instance, a non-stochasticiterative algorithm to determine the first ML clustering model based onthe first plurality of textual records; (iii) receiving, by a secondprediction computational instance and from the end-user computationalinstance, a second textual record; (iv) determining, by the secondprediction computational instance, that the second predictioncomputational instance does not contain an up-to-date ML clusteringmodel corresponding to the end-user computational instance; (v)responsive to determining that the second prediction computationalinstance does not contain an up-to-date ML clustering modelcorresponding to the end-user computational instance, receiving, by thesecond prediction computational instance and from the end-usercomputational instance, the first plurality of textual records, anindication of an ordering of textual records within the first pluralityof textual records, and the first textual record; (vi) using, by thesecond prediction computational instance, the non-stochastic iterativealgorithm to determine, based on the first plurality of textual recordsand the first textual record, a second ML clustering model thatrepresents a set of clusters of textual records, wherein using thenon-stochastic iterative algorithm to determine the second ML clusteringmodel includes using each textual record in the first plurality oftextual records and the first textual record, in order, to update thesecond ML clustering model via the non-stochastic iterative algorithm;(vii) determining, by the second ML clustering model, that the secondtextual record corresponds to a particular cluster selected from the setof clusters of textual records; and (viii) transmitting, by the secondprediction computational instance and to the end-user computationalinstance, a representation of the particular cluster. Using thenon-stochastic iterative algorithm to determine the first ML clusteringmodel includes using each textual record in the first plurality oftextual records, in order, to update the first ML clustering model viathe non-stochastic iterative algorithm.

In some examples, determining, by the ML clustering model, that thefirst textual record corresponds to the particular cluster selected fromthe set of clusters of textual records includes at least one of: (i)using the ML model to determine word vectors that describe, in a firstsemantically-encoded vector space, a meaning of respective words of thefirst textual record and comparing the word vectors to at least one of alocation or a volume, within the first semantically-encoded vectorspace, that corresponds to the particular cluster; or (ii) using the MLmodel to determine a paragraph vector that describes, in a secondsemantically-encoded vector space, a meaning of multiple words of thefirst textual record and comparing the paragraph vector to at least oneof a location or a volume, within the second semantically-encoded vectorspace.

In some examples, the ML clustering model includes a respectivelocation, within a vector space, of each set of clusters of textualrecords represented by the ML clustering model. In such examples,determining, by the ML clustering model, that the first textual recordcorresponds to the particular cluster selected from the set of clustersof textual records can include: (i) determining, based on the firsttextual record, a first textual record location within the vector space,and (ii) determining that a distance, within the vector space, betweenthe first textual record location and a location of the particularcluster is less than a threshold distance

FIG. 14 is a flow chart illustrating an example embodiment. The processillustrated by FIG. 14 may be carried out by a computing device, such ascomputing device 100, and/or a cluster of computing devices, such asserver cluster 200. However, the process can be carried out by othertypes of devices or device subsystems. For example, the process could becarried out by a portable computer, such as a laptop or a tablet device.

The embodiments of FIG. 14 may be simplified by the removal of any oneor more of the features shown therein. Further, these embodiments may becombined with features, aspects, and/or implementations of any of theprevious figures or otherwise described herein.

The example method of FIG. 14 includes obtaining a plurality of textualrecords divided into clusters and a residual set of the textual records,wherein an ML clustering model divided the plurality of textual recordsbased on a similarity metric (1400). The example method of FIG. 14additionally includes receiving, from a client device, a particulartextual record representing a query (1402). The example method of FIG.14 also includes determining, by way of the ML clustering model andbased on the similarity metric, that the particular textual record doesnot fit into any of the clusters (1404). The example method of FIG. 14yet further includes adding the particular textual record to theresidual set of the textual records in response to determining that theparticular textual record does not fit into any of the clusters (1406).The example method of FIG. 14 could include additional or alternativesteps. In some examples, the example method of FIG. 14 couldadditionally include: (i) receiving, from the client device, a secondparticular textual record representing a second query; (ii) determining,by way of the ML clustering model and based on the similarity metric,that the second particular textual record fits into a particular clusterof the clusters; and (iii) in response to determining that the secondparticular textual record fits into the particular cluster of theclusters, adding the second particular textual record to the particularcluster. In such examples, determining that the particular textualrecord fits into the particular cluster can include determining that thesimilarity metric indicates that the particular textual record fits intothe particular cluster better than the particular textual record fitsinto all other clusters. Additionally or alternatively, determining thatthe particular textual record fits into the particular cluster caninclude determining that the similarity metric indicates that theparticular textual record fits into the particular cluster to a degreethat exceeds a specified threshold similarity. Additionally oralternatively, the ML clustering model can include a respectivelocation, within a vector space, of each of the clusters of the textualrecords and determining that the second particular textual record fitsinto the particular cluster can include: (i) determining, based on thesecond particular textual record, a second textual record locationwithin the vector space, and (ii) determining that a distance, withinthe vector space, between the second textual record location and alocation of the particular cluster is less than a threshold distance.

In some examples, the example method of FIG. 14 could additionallyinclude: (i) identifying, by way of the ML clustering model, that theresidual set of the textual records contains a further cluster; (ii)adding the further cluster to the clusters of the textual records; and(iii) removing constituent textual records of the further cluster fromthe residual set of the textual records. In some examples, determiningthat the particular textual record does not fit into any of the clusterscan include at least one of: (i) using the ML clustering model todetermine word vectors that describe, in a first semantically-encodedvector space, a meaning of respective words of the particular textualrecord and comparing the word vectors to at least one of locations orvolumes, within the first semantically-encoded vector space, thatrespectively correspond to the clusters; or (ii) using the ML model todetermine a paragraph vector that describes, in a secondsemantically-encoded vector space, a meaning of multiple words of theparticular textual record and comparing the paragraph vector to at leastone of locations or volumes, within the second semantically-encodedvector space, that respectively correspond to the clusters.

X. Conclusion

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its scope, as will be apparent to thoseskilled in the art. Functionally equivalent methods and apparatuseswithin the scope of the disclosure, in addition to those describedherein, will be apparent to those skilled in the art from the foregoingdescriptions. Such modifications and variations are intended to fallwithin the scope of the appended claims.

The above detailed description describes various features and operationsof the disclosed systems, devices, and methods with reference to theaccompanying figures. The example embodiments described herein and inthe figures are not meant to be limiting. Other embodiments can beutilized, and other changes can be made, without departing from thescope of the subject matter presented herein. It will be readilyunderstood that the aspects of the present disclosure, as generallydescribed herein, and illustrated in the figures, can be arranged,substituted, combined, separated, and designed in a wide variety ofdifferent configurations.

With respect to any or all of the message flow diagrams, scenarios, andflow charts in the figures and as discussed herein, each step, block,and/or communication can represent a processing of information and/or atransmission of information in accordance with example embodiments.Alternative embodiments are included within the scope of these exampleembodiments. In these alternative embodiments, for example, operationsdescribed as steps, blocks, transmissions, communications, requests,responses, and/or messages can be executed out of order from that shownor discussed, including substantially concurrently or in reverse order,depending on the functionality involved. Further, more or fewer blocksand/or operations can be used with any of the message flow diagrams,scenarios, and flow charts discussed herein, and these message flowdiagrams, scenarios, and flow charts can be combined with one another,in part or in whole.

A step or block that represents a processing of information cancorrespond to circuitry that can be configured to perform the specificlogical functions of a herein-described method or technique.Alternatively or additionally, a step or block that represents aprocessing of information can correspond to a module, a segment, or aportion of program code (including related data). The program code caninclude one or more instructions executable by a processor forimplementing specific logical operations or actions in the method ortechnique. The program code and/or related data can be stored on anytype of computer readable medium such as a storage device including RAM,a disk drive, a solid state drive, or another storage medium.

The computer readable medium can also include non-transitory computerreadable media such as computer readable media that store data for shortperiods of time like register memory and processor cache. The computerreadable media can further include non-transitory computer readablemedia that store program code and/or data for longer periods of time.Thus, the computer readable media may include secondary or persistentlong term storage, like ROM, optical or magnetic disks, solid statedrives, compact-disc read only memory (CD-ROM), for example. Thecomputer readable media can also be any other volatile or non-volatilestorage systems. A computer readable medium can be considered a computerreadable storage medium, for example, or a tangible storage device.

Moreover, a step or block that represents one or more informationtransmissions can correspond to information transmissions betweensoftware and/or hardware modules in the same physical device. However,other information transmissions can be between software modules and/orhardware modules in different physical devices.

The particular arrangements shown in the figures should not be viewed aslimiting. It should be understood that other embodiments can includemore or less of each element shown in a given figure. Further, some ofthe illustrated elements can be combined or omitted. Yet further, anexample embodiment can include elements that are not illustrated in thefigures.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purpose ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

What is claimed is:
 1. A remote network management platform comprising:an end-user computational instance that is dedicated to a managednetwork; and a prediction computational instance that is configured toperform operations including: receiving, from the end-user computationalinstance, a first textual record, determining, by a machine learning(ML) clustering model that represents a set of clusters of textualrecords, wherein each textual record within a cluster of the set ofclusters and each cluster of the set of clusters has a respectivelocation within a vector space, that the first textual recordcorresponds to a particular cluster in the set of clusters of textualrecords by: determining, based on the first textual record, a locationof the first textual record within the vector space, wherein thelocation of the first textual record is based on a time of generation ofthe first textual record and a time of resolution of the first textualrecord, and determining that a distance, within the vector space,between the location of the first textual record and the respectivelocation of the particular cluster is less than a threshold distance,transmitting, to the end-user computational instance, a representationof the particular cluster, receiving, from the end-user computationalinstance, a second textual record, determining, by the ML clusteringmodel, that the second textual record does not correspond to any clusterof textual records of the set of clusters of textual records, responsiveto determining that the second textual record does not correspond to anycluster of textual records of the set of clusters of textual records,adding the second textual record to a stored set of residual textualrecords, identifying an additional cluster of textual records based onthe stored set of residual textual records, and transmitting, to theend-user computational instance, a representation of the additionalcluster of textual records.
 2. The remote network management platform ofclaim 1, wherein the operations performed by the predictioncomputational instance additionally include: prior to receiving thefirst textual record, receiving, from the end-user computationalinstance, the ML clustering model.
 3. The remote network managementplatform of claim 1, wherein the operations performed by the predictioncomputational instance additionally include: expanding the ML clusteringmodel to additionally represent the additional cluster of textualrecords.
 4. The remote network management platform of claim 3, whereinthe operations performed by the prediction computational instanceadditionally include: transmitting, to the end-user computationalinstance, the ML clustering model as expanded.
 5. The remote networkmanagement platform of claim 1, wherein the prediction computationalinstance is a first prediction computational instance and the MLclustering model is a first ML clustering model, and wherein theoperations performed by the first prediction computational instanceadditionally include: prior to receiving the first textual record,receiving, from the end-user computational instance, a first pluralityof textual records, and using a non-stochastic iterative algorithm todetermine the first ML clustering model based on the first plurality oftextual records, wherein using the non-stochastic iterative algorithm todetermine the first ML clustering model comprises using each textualrecord in the first plurality of textual records, in order, to updatethe first ML clustering model via the non-stochastic iterativealgorithm; wherein the remote network management platform furthercomprises a second prediction computational instance that is configuredto perform operations including: receiving, from the end-usercomputational instance, a third textual record, determining that thesecond prediction computational instance does not contain an up-to-dateML clustering model corresponding to the end-user computationalinstance, responsive to determining that the second predictioncomputational instance does not contain an up-to-date ML clusteringmodel corresponding to the end-user computational instance, receiving,from the end-user computational instance, the first plurality of textualrecords, an indication of an ordering of textual records within thefirst plurality of textual records, and the first textual record, usingthe non-stochastic iterative algorithm to determine, based on the firstplurality of textual records and the first textual record, a second MLclustering model that represents a second set of clusters of textualrecords, wherein using the non-stochastic iterative algorithm todetermine the second ML clustering model comprises using each textualrecord in the first plurality of textual records and the first textualrecord, in order, to update the second ML clustering model via thenon-stochastic iterative algorithm, determining, by the second MLclustering model, that the third textual record corresponds to a secondparticular cluster in the second set of clusters of textual records, andtransmitting, to the end-user computational instance, a representationof the second particular cluster.
 6. The remote network managementplatform of claim 1, wherein the ML clustering model is a first MLclustering model, and wherein the operations performed by the predictioncomputational instance additionally include: receiving, from theend-user computational instance, additional textual records, using eachof the additional textual records to update the first ML clusteringmodel via an iterative update process, determining that a model refreshcriterion is satisfied, responsive to determining that the model refreshcriterion is satisfied, determining, based on the additional textualrecords, a second ML clustering model that represents a second set ofclusters of textual records, wherein each cluster of the second MLclustering model corresponds to a respective set of textual recordswithin the additional textual records.
 7. The remote network managementplatform of claim 6, wherein the operations performed by the predictioncomputational instance additionally include: receiving an indication ofa preferred cluster of a first set of clusters of textual recordsrepresented by the first ML clustering model, wherein determining thesecond ML clustering model based on the additional textual recordscomprises determining a first cluster within a second set of clusters oftextual records of the second ML clustering model based on the preferredcluster.
 8. The remote network management platform of claim 1, whereinthe vector space comprises a first semantically-encoded vector space anda second semantically-encoded vector space, wherein determining, by theML clustering model, that the first textual record corresponds to theparticular cluster selected from the set of clusters of textual recordscomprises at least one of: (i) using the ML model to determine wordvectors that describe, in the first semantically-encoded vector space,meanings of respective words of the first textual record and comparingthe word vectors to at least one of the location or a volume, within thefirst semantically-encoded vector space, that corresponds to theparticular cluster; or (ii) using the ML model to determine a paragraphvector that describes, in the second semantically-encoded vector space,a meaning of multiple words of the first textual record and comparingthe paragraph vector to at least one of the location or the volume,within the second semantically-encoded vector space, that corresponds tothe particular cluster.
 9. A computer-implemented method comprising:receiving, by a prediction computational instance and from an end-usercomputational instance, a first textual record, wherein the end-usercomputational instance is dedicated to a managed network, and whereinthe prediction computational instance and the end-user computationalinstance are both disposed within a remote network management platform;determining, by a machine learning (ML) clustering model of theprediction computational instance that represents a set of clusters oftextual records, wherein each textual record within a cluster of the setof clusters and each cluster of the set of clusters has a respectivelocation within a vector space, that the first textual recordcorresponds to a particular cluster selected from the set of clusters oftextual records by: determining, based on the first textual record, alocation of the first textual record within the vector space, whereinthe location of the first textual record is based on a time ofgeneration of the first textual record and a time of resolution of thefirst textual record; and determining that a distance, within the vectorspace, between the location of the first textual record and therespective location of the particular cluster is less than a thresholddistance; transmitting, by the prediction computational instance and tothe end-user computational instance, a representation of the particularcluster; receiving, by the prediction computational instance and fromthe end-user computational instance, a second textual record;determining, by the ML clustering model, that the second textual recorddoes not correspond to any cluster of textual records of the set ofclusters of textual records; responsive to determining that the secondtextual record does not correspond to any cluster of textual records ofthe set of clusters of textual records, adding, by the predictioncomputational instance, the second textual record to a stored set ofresidual textual records; identifying, by the prediction computationalinstance, an additional cluster of textual records based on the storedset of residual textual records; and transmitting, by the predictioncomputational instance and to the end-user computational instance, arepresentation of the additional cluster of textual records.
 10. Thecomputer-implemented method of claim 9, wherein the predictioncomputational instance is a first prediction computational instance andthe ML clustering model is a first ML clustering model, thecomputer-implemented method further comprising: receiving, by the firstprediction computational instance and from the end-user computationalinstance prior to receiving the first textual record, a first pluralityof textual records; prior to receiving the first textual record, using,by the first prediction computational instance, a non-stochasticiterative algorithm to determine the first ML clustering model based onthe first plurality of textual records, wherein using the non-stochasticiterative algorithm to determine the first ML clustering model comprisesusing each textual record in the first plurality of textual records, inorder, to update the first ML clustering model via the non-stochasticiterative algorithm; receiving, by a second prediction computationalinstance and from the end-user computational instance, a second textualrecord; determining, by the second prediction computational instance,that the second prediction computational instance does not contain anup-to-date ML clustering model corresponding to the end-usercomputational instance; responsive to determining that the secondprediction computational instance does not contain an up-to-date MLclustering model corresponding to the end-user computational instance,receiving, by the second prediction computational instance and from theend-user computational instance, the first plurality of textual records,an indication of an ordering of textual records within the firstplurality of textual records, and the first textual record; using, bythe second prediction computational instance, the non-stochasticiterative algorithm to determine, based on the first plurality oftextual records and the first textual record, a second ML clusteringmodel that represents a second set of clusters of textual records,wherein using the non-stochastic iterative algorithm to determine thesecond ML clustering model comprises using each textual record in thefirst plurality of textual records and the first textual record, inorder, to update the second ML clustering model via the non-stochasticiterative algorithm; determining, by the second ML clustering model,that the second textual record corresponds to a particular clusterselected from the second set of clusters of textual records; andtransmitting, by the second prediction computational instance and to theend-user computational instance, a representation of the particularcluster.
 11. The computer-implemented method of claim 9, wherein thevector space comprises a first semantically-encoded vector space and asecond semantically-encoded vector space, wherein determining, by the MLclustering model, that the first textual record corresponds to theparticular cluster selected from the set of clusters of textual recordsfurther comprises at least one of: (i) using the ML model to determineword vectors that describe, in the first semantically-encoded vectorspace, a meaning of respective words of the first textual record andcomparing the word vectors to at least one of the location or a volume,within the first semantically-encoded vector space, that corresponds tothe particular cluster; or (ii) using the ML model to determine aparagraph vector that describes, in the second semantically-encodedvector space, a meaning of multiple words of the first textual recordand comparing the paragraph vector to at least one of the location orthe volume, within the second semantically-encoded vector space.
 12. Thecomputer-implemented method of claim 9, comprising: receiving, by theprediction computational instance, the ML clustering model from theend-user computational instance prior to receiving the first textualrecord.
 13. The computer-implemented method of claim 9, comprising:expanding, by the prediction computational instance, the ML clusteringmodel to additionally represent the additional cluster of textualrecords.
 14. The computer-implemented method of claim 13, comprising:transmitting, by the prediction computational instance, the MLclustering model as expanded to the end-user computational instance. 15.A computer-implemented method comprising: obtaining a plurality oftextual records divided into a plurality of clusters and a residual setof textual records, wherein each textual record of the plurality oftextual records, each cluster of the plurality of clusters, and eachtextual record of the residual set of textual records has a respectivelocation within a vector space, and wherein a machine learning (ML)clustering model is configured to divide the plurality of textualrecords into the plurality of clusters based on a similarity metric;receiving, from a client device, a particular textual recordrepresenting a query; determining, by way of the ML clustering model andbased on the similarity metric, that the particular textual record doesnot fit into any of the plurality of clusters by: determining, based onthe particular textual record, a location of the particular textualrecord, wherein the location of the particular textual record is basedon a time of generation and a time of resolution of the particulartextual record; and determining that a distance, within the vectorspace, between the location of the particular textual record and therespective location of each of the plurality of clusters is greater thana threshold distance; and in response to determining that the particulartextual record does not fit into any of the plurality of clusters,adding the particular textual record to the residual set of textualrecords.
 16. The computer-implemented method of claim 15, furthercomprising: receiving, from the client device, a second particulartextual record representing a second query; determining, by way of theML clustering model and based on the similarity metric, that the secondparticular textual record fits into a particular cluster of theplurality of clusters by: determining, based on the second particulartextual record, a location of the second particular textual record,wherein the location of the second particular textual record is based ona time of generation and a time of resolution of the second particulartextual record; and determining that a second distance, within thevector space, between the location of the second particular textualrecord and the respective location of the particular cluster is lessthan the threshold distance; and in response to determining that thesecond particular textual record fits into the particular cluster of theclusters, adding the second particular textual record to the particularcluster.
 17. The computer-implemented method of claim 16, whereindetermining that the particular textual record fits into the particularcluster comprises determining that the similarity metric indicates thatthe particular textual record fits into the particular cluster betterthan the particular textual record fits into all other clusters.
 18. Thecomputer-implemented method of claim 16, wherein determining that theparticular textual record fits into the particular cluster comprisesdetermining that the similarity metric indicates that the particulartextual record fits into the particular cluster to a degree that exceedsa specified threshold similarity.
 19. The computer-implemented method ofclaim 15, further comprising: identifying, by way of the ML clusteringmodel, that the residual set of textual records contains a furthercluster; adding the further cluster to the plurality of clusters; andremoving constituent textual records of the further cluster from theresidual set of textual records.
 20. The computer-implemented method ofclaim 15, wherein the vector space comprises a firstsemantically-encoded vector space and a second semantically-encodedvector space, wherein determining that the particular textual recorddoes not fit into any of the plurality of clusters further comprises atleast one of: (i) using the ML clustering model to determine wordvectors that describe, in the first semantically-encoded vector space, ameaning of respective words of the particular textual record andcomparing the word vectors to at least one of locations or volumes,within the first semantically-encoded vector space, that respectivelycorrespond to the plurality of clusters; or (ii) using the ML model todetermine a paragraph vector that describes, in the secondsemantically-encoded vector space, a meaning of multiple words of theparticular textual record and comparing the paragraph vector to at leastone of locations or volumes, within the second semantically-encodedvector space, that respectively correspond to the plurality of clusters.